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About Air Apps At Air Apps, we believe in thinking bigger—and moving faster. We're a family-founded company on a mission to create the world's first AI-powered Personal & Entrepreneurial Resource Planner (PRP), and we need your passion and ambition to help us change how people plan, work, and live. Born in Lisbon, Portugal in 2018—and now with offices in both Lisbon and San Francisco—we've remained self-funded while reaching over 100 million downloads worldwide . Our long-term focus drives us to challenge the status quo every day, pushing the boundaries of AI-driven solutions that truly make a difference. Here, you'll be a creative force, shaping products that empower people across the glob e. Join us on this journey to redefine resource management—and change lives along the w ay. The Role A s an AI/ML Eng ineer, you will play a crucial role in designing, developing, and optimizing machine learning models to power our mobile applications. You will work closely with product managers, engineers, and designers to create intelligent, data-driven features that enhance user experiences. Your expertise in artificial intelligence and deep learning will help us innovate and stay ahead in the mobile app indu stry. Please note that this post serves the purpose of enhancing our talent pool while we prepare to launch the official job. As soon as it gets posted we will get in touch wit h you. Responsib ilities Develop, train, and optimize machine learning models for various mobile app f eatures.Research and implement state-of-the-art AI techniques to improve user engagement and app perf ormance.Collaborate with cross-functional teams to integrate AI-driven solutions into our appli cations.Design and maintain scalable ML pipelines, ensuring efficient model deployment and mon itoring.Analyze large datasets to derive insights and drive data-driven decision -making.Stay updated with the latest AI trends and best practices, incorporating them into our development pr ocesses.Optimize AI models for mobile environments to ensure high performance and low latency. Req uirements Around 3+ years of experience in AI/ML development, preferably in mobile app lications.Proficiency in Python, TensorFlow, PyTorch, or other ML f rameworks.Experience with deep learning, NLP, computer vision, and statistical modeling.Familiarity with cloud-based ML services (AWS, Google Cloud, or Azure).Strong understanding of data structures, algorithms, and software engineering best practices.Experience in deploying and maintaining ML models in p roduction.Ability to work collaboratively in a remote team en vironment.Strong problem-solving skills and a passion for i nnovation. What benefits d o we offer? Apple hardware ecosyst em for work.A nnual Bonus.Medical Insurance (including vision &a mp; dental).Disability insurance - short an d long-term.401k up to 4% c ontribution.Air Conference – an opportunity to meet the team, collaborate, and gr ow together.Transport ation budgetFree meal s at the hubGy m membership Diversity &a mp; Inclusion At Air Apps, we are committed to fostering a diverse, inclusive, and equitable workplace. We enthusiastically welcome applicants from all backgrounds, experiences, and perspectives. We celebrate diversity in all its forms and believe that varied voices and experiences mak e us stronger. Applica tion Disclaimer At Air Apps, we value transparency and integrity in our hiring process. Applicants must submit their own work without any AI-generated assistance. Any use of AI in application materials, assessments, or interviews will result in d isqualification. Compensation Ran ge: $100K - $187K

Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that streamline the auto financing process for dealers. Our team focuses on creating integrated, secure, and user friendly platforms that enhance dealer operations and customer experiences, ensuring compliance with the latest financial regulations. This pivotal role supports business growth and fosters strong dealer relationships, making every transaction smoother and more efficient. Dealer Tech within Financial Services Technology at Capital One is specifically designed to address the technological needs of auto dealers who partner with Capital One. This division focuses on developing and maintaining systems that facilitate the smooth operation of auto financing, from loan origination to funding. Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 3 years of experience programming with Python, Java, Golang, or C++ At least 2 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 3 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 1 year of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 1+ years of experience optimizing ML algorithms, configurations, and infrastructure 1+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 1+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 1+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) Authored/co-authored a paper on a ML technique, model, or proof of concept 1+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $161,800 - $184,600 for Machine Learning Engineer 3 New York, NY: $176,500 - $201,400 for Machine Learning Engineer 3 Plano, TX: $147,100 - $167,900 for Machine Learning Engineer 3 Richmond, VA: $147,100 - $167,900 for Machine Learning Engineer 3 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we're behind some of Spotify's most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you'll keep millions of users listening by making great recommendations to each and every one of them. The Surfaces Podcasts team owns podcast recommendations across some of Spotify's most visible surfaces, including Home and the Now Playing view. We build and improve the recommendation systems that help listeners discover their next favorite show or episode while helping creators reach the right audiences. As a Staff Machine Learning Engineer, you'll help shape the future of personalized discovery and engagement at Spotify. You'll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences for listeners at global scale. You'll take models from research to production, help set technical direction in ambiguous problem spaces, and solve complex personalization challenges across Spotify. What You'll Do Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience. Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally. Build content recommendation systems for emerging agentic and AI-powered user experiences. Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches. Partner closely with Product Managers, Engineers, Data Scientists, and Designers to define and execute experimentation strategies. Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency. Improve ML platform capabilities, data pipelines, and production systems that support personalization at Spotify scale. Drive technical direction in ambiguous problem spaces and contribute to the long-term architecture of our personalization systems. Mentor and support other Machine Learning Engineers, sharing knowledge and helping strengthen engineering practices across the team. Who You Are You have deep experience building and deploying machine learning systems in production environments. You have deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms. You are proficient in Python and have hands-on experience building machine learning systems with PyTorch. You have experience with large language model training, fine-tuning, evaluation, and optimization techniques, including SFT, distillation, and LoRA. You have worked with large-scale inference systems and understand the challenges of latency, reliability, and cost optimization. You care about creating high-quality user experiences through thoughtful applications of machine learning. You communicate effectively with technical and non-technical partners and can influence technical decisions beyond your immediate team. You know how to design, execute, and interpret online experiments and A/B tests to improve user outcomes. You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks. You have experience building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms. Where You'll Be We offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location. This team operates within the Eastern Standard time zone for collaboration. The United States base range for this position is $227,495- $324,993 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future. Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what's playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It's in our differences that we will find the power to keep revolutionizing the way the world listens. At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we're here to support you in any way we can. Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world's most popular audio streaming subscription service.

Responsibilities We are a group of applied machine learning engineers and data scientists that focus on general feed recommendations and E-commerce recommendations. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions. We are interested and excited in applying large scale machine learning to solve various real-world problems. What you will do: • Participate in building large-scale (10 million to 100 million) recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok. • Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently. • Design, develop, evaluate and iterate on predictive models for candidate generation and ranking(eg. Click Through Rate and Conversion Rate prediction) , including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation. • Design and build supporting/debugging tools as needed. Qualifications Minimum Qualifications • Bachelor's degree or higher in Computer Science or related fields. • Strong programming and problem-solving ability. • Experience in applied machine learning, familiar with one or more of the algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, Wide and Deep etc. • Experience in Deep Learning Tools such as tensorflow/pytorch. • Experience with at least one programming language like C++/Python or equivalent. Preferred Qualifications: • Experience in recommendation system, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields. • Publications at KDD, NeurlPS, WWW, SIGIR, WSDM, ICML, IJCAI, AAAI, RECSYS and related conferences/journals, or experience in data mining/machine learning competitions such as Kaggle/KDD-cup etc. About USDS TikTok USDS Joint Venture LLC is dedicated to the safety and security of millions of Americans who create, discover, and connect with what they love on the apps we operate. The Joint Venture has been established in compliance with the Executive Order signed by President Trump on September 25, 2025. Our foundation is a comprehensive data privacy and cybersecurity program we operate under defined safeguards to protect national security and secure U.S. user data, apps and the algorithm. We safeguard the U.S. content ecosystem, holding decision-making authority for trust and safety policies and moderation. USDS Joint Venture helps ensure Americans can continue to express their creativity, discover new hobbies and interests, and build thriving communities and businesses on a global scale.​ ​ On-site presence across teams allows the company to operate with greater speed, alignment, and agility — especially in areas like real-time decision-making, team development, and integrated execution. As such, the company is shifting from a hybrid work model to a fully in-person schedule up to 5 days a week.​ Why Join Us Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.​ We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.​ ​ Diversity & Inclusion​ TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.​ USDS Reasonable Accommodation USDS is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/USDS-RA​ Job Information 【For Pay Transparency】Compensation Description (Annually) The base salary range for this position in the selected city is $129960 - $246240 annually.​ Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.​ Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).​ The Company reserves the right to modify or change these benefits programs at any time, with or without notice.​ For Los Angeles County (unincorporated) Candidates:​ Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:​ Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;​ Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and​ Exercising sound judgment.​

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About The Team The Radar ML team builds the machine learning systems that power Stripe's Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce - helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe. The team is growing, and expanding into multiple new areas. We are building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for 50+ global payment methods. What you'll do You will own ML problems from conception to production: analyzing new fraud and abuse patterns, exploring large-scale datasets, experimenting with model architectures, designing evaluations, deploying models, and monitoring performance. Your work may include developing transformer architectures for payment data, building graph-powered intelligence across consumers, leveraging LLMs to detect emerging attacks, or rapidly prototyping new protections with frontier AI labs. You will have opportunities to ship 0→1 products, and to improve mission-critical ML models operating at Stripe scale. Responsibilities Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe's global network. Investigate emerging fraud and abuse patterns, identify where ML can help, and rapidly develop effective solutions. Design features, datasets, evaluation methods, and experiments for noisy, delayed, and adversarial environments. Improve model architectures, training methods, and production systems to increase model quality, coverage, and adaptability. Monitor production behavior and improve models as data, customer needs, and attack patterns change. Balance model quality with legitimate-user impact, latency, reliability, and operational complexity. Partner with engineering, product, data science, risk experts, and leading Stripe users to turn modeling improvements into differentiated Radar products. Contribute to reusable tools and AI-assisted workflows that help the team develop and operate ML systems more effectively. Who you are You are a fast, pragmatic problem solver with deep ML expertise and a bias toward action. You are excited to work at the frontier of payments and AI - turning advances in machine learning into production systems that address new forms of fraud and abuse. You've built ML models and shepherded them to production, landing impact for users. We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum Requirements 2+ years of experience developing, evaluating, and deploying machine learning models in a production environment. Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design. Strong programming and software-engineering skills, with experience writing tested, maintainable code for production ML applications. Experience building or operating production ML systems, including one or more of large-scale data and feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring. Ability to investigate ambiguous problems, reason from data, and choose pragmatic technical approaches. Ability to communicate technical ideas and results clearly and collaborate effectively across disciplines. Preferred Qualifications Experience owning an ML problem across the full lifecycle, from problem framing and data exploration through deployment, monitoring, and iteration. Experience developing or productionizing modern deep learning, graph-learning, or LLM-based approaches. Experience building and optimizing real-time, low-latency ML systems at scale. Experience applying ML to fraud detection, integrity, trust and safety, identity, cybersecurity, or another adversarial domain. Experience rapidly building 0→1 products, working directly with customers, or operating effectively in a fast-moving environment. Active interest in the latest ML developments, and how they can be leveraged to solve business problems. Hybrid work at Stripe This role is available either in an office or a remote location (35+ miles or 56+ km from a Stripe office). In-office expectations Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams. Working remotely at Stripe A remote location is defined as being 35 miles (56 kilometers) or more from one of our offices. While you would be welcome to come into the office for team/business meetings, on-sites, meet-ups, and events, our expectation is you would regularly work from home rather than a Stripe office. Stripe does not cover the cost of relocating to a remote location. We encourage you to apply for roles that match the location where you currently live or plan to live. Pay and benefits The annual US base salary range for this role is $180,000 - $270,000. For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate's experience, qualifications, and location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process. Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends. We look forward to hearing from you. At Stripe, we're looking for people with passion, grit, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Stripe, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us. Apply now Please find our California applicant personal information notice here. The application window will remain open for 100 days after the Job Post is published. However, this opportunity will remain open based on the needs of the business, which may cause the application window to close before or after the 100-day mark.

About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We're Most Proud Of Offering life-changing career opportunities to talented software professionals across the Americas. Building highly-skilled software development teams for hundreds of the world's greatest companies. Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. Our 4.1-star rating on GlassDoor. Our client Net Promoter Score of 68, twice the industry average. The Position We're Looking To Hire a Senior Machine Learning Engineer (Traditional ML & Financial Data) To Join Our Team. You'll Work With Our Incredible Clients In One Of Two Ways Team Augmentation: You will integrate yourself directly into our client's team and work alongside their existing designers and engineers on a daily basis. Design & Build: You will work on a FullStack product team to build and deliver a product to our clients. What We're Looking For 5+ years of professional experience in software development, Data Science, or Machine Learning Engineering. Advanced English is required. Successful completion of a four-year college degree is required. Strong expertise in traditional machine learning algorithms and statistical modeling, with a focus on non-LLM and non-Generative AI approaches. Hands-on experience designing, developing, training, and optimizing machine learning models for production use. Strong proficiency in data exploration, preprocessing, feature engineering, and model training using complex datasets. Demonstrated experience developing and optimizing anomaly detection algorithms to identify irregularities, patterns, and outliers in data. Strong Python skills and experience with relevant machine learning and data science libraries. Experience analyzing financial, payroll, or other structured business datasets is highly preferred. Proactive, ownership-driven mindset with strong analytical and problem-solving skills, capable of translating complex data into reliable machine learning solutions. Ability to work through new and difficult issues and contribute to libraries as needed. Ability to create and maintain continuous integration and delivery of applications. Forensic attention to detail. A positive mindset and a can-do attitude. Experience working on Agile/Scrum teams. Meaningful experience working on large, complex systems. Ability to take extreme ownership over your work. Every day is a challenge to ensure you are performing to the expectations you and your team have agreed upon. Ability to identify with the goals of FullStack's clients, and dedicate yourself to delivering on the commitments you and your team make to them. Nice To Have We're Seeking Engineers Who Are AI-enthusiastic In Their Workflow. Professional Experience Or Personal Project Work With The Following Is a Significant Plus Cursor, Windsurf, or Google Antigravity. Claude Code, Gemini CLI, Codex, or Copilot CLI. Work Authorization Requirements Applicants must be currently authorized to work in the United States on a full-time basis. FullStack will not sponsor applicants for work visas now or in the future. What We Offer Competitive Salary. Paid Time Off (vacation, sick leave, parental leave, and holidays). 100% remote work. The ability to work with leading startups and Fortune 500 companies. Health, dental, and vision insurance. 401(k) w/ 4% match. Ample opportunity for career advancement. Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

#WeAreParamount on a mission to unleash the power of content… you in? We've got the brands, we've got the stars, we've got the power to achieve our mission to entertain the planet – now all we're missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture. Overview and Responsibilities: The Signal Intelligence (SigInt) team turns a high-volume, multi-source data ecosystem — operational systems, client-app event streams, ad-tech platforms, stitcher events, ad server logs, and telemetry — into the trusted data models, metrics, and dashboards that drive business, product, and ad-ops decisions. We are hiring a Senior Data Engineer who is, in equal measure, a data modeler, a SQL craftsperson, and an analyst — not only a pipeline builder. You will own data end-to-end: modeling raw events into conformed, analytics-ready marts; writing the analytical SQL that answers hard business questions; defining the metrics leadership relies on; and building the pipelines that feed it all. Because we sit close to the business, translating ambiguous questions into durable models and clear insight matters as much as engineering the pipeline. In practice, you will be the team's data sherpa — the person who knows the data terrain cold, guides analysts and business partners through it, and turns an ambiguous "can we find out X?" into a trusted, well-modeled answer. You own the map — the definitions, lineage, and the "why" behind the numbers — as much as the pipelines. This is an analytics-engineering role on a semi-business team. It is not a pure platform / infrastructure position — if your passion is exclusively distributed-systems plumbing with little interest in modeling, analysis, or guiding the business, this will not be the right fit. Responsibilities Include: Modeling & architecture — the core of the role Design and own the dimensional and semantic models — conformed dimensions, facts, star/snowflake schemas, slowly changing dimensions (Type 2), and well-defined grain — that serve as the trusted source of truth. Architect data marts spanning operational, advertising, and telemetry domains for analytics, reporting, AI, and operational use. Establish shared definitions and a semantic / metrics layer so users, sessions, devices, content, campaigns, and impressions mean the same thing everywhere. SQL & analysis Write and optimize sophisticated analytical SQL — window functions, cohort and funnel logic, sessionization, distributions — on large event-scale datasets. Analyze complex datasets to find patterns, quantify drivers, and produce actionable insight; define new metrics and KPIs (usage, revenue, etc.) and the logic behind them. Independently investigate variance across platforms and communicate findings clearly to peers and senior leadership. Business partnership Be the go-to guide for the data — the person who knows where every number comes from, what it means, and its quirks and landmines. Own the institutional knowledge of the data — definitions, lineage, and the reasoning behind metrics — and make it legible so analysts, ad ops, product, and finance can self-serve with confidence. Partner with stakeholders and source-system owners to translate ambiguous business questions into durable data models and metrics, and support/conduct strategic investigations and analysis. Pipelines, quality & delivery Build and harmonize the ingestion and transformation pipelines — event data from client apps, ad-tech platforms, stitchers, ad servers, and telemetry — that feed the models, handling deduplication, late-arriving data, sessionization, and schema evolution. Stitch identity and sessions across client, server, and ad-side events for accurate user, content, and revenue analytics. Establish data-quality standards (testing, monitoring, alerting, freshness and volume SLAs) and troubleshoot incidents end-to-end — from a dashboard anomaly back through marts, transformations, and raw logs. Support and improve BI dashboards; document datasets, lineage, and business logic so the data map stays trustworthy and current. Mentor engineers and analysts on SQL, modeling, event data, and engineering best practices. Basic Qualifications Expert SQL, including analytical SQL craft (window functions, complex joins, cohort / funnel / sessionization logic) and performance tuning on large event-scale datasets. Deep data-modeling expertise — dimensional modeling, star/snowflake schemas, slowly changing dimensions, conformed dimensions, grain, and data-mart design; able to design a trusted source of truth from messy multi-source data. Strong data analysis and business translation — able to independently investigate data, define metrics and KPIs, and act as a trusted guide who turns ambiguous business questions into clear answers and enables others to self-serve. A guide's temperament — curiosity to learn the data terrain deeply, patience to help non-technical partners, and communication that earns trust across the business. dbt (or an equivalent transformation / modeling framework). Cloud warehouse — BigQuery strongly preferred (our environment); Snowflake or Databricks acceptable with a fast ramp. Python for data processing and pipeline development. Orchestration experience (Airflow). High-volume event data — clickstream, telemetry, ad impressions, or similar — including deduplication, late-arriving data, sessionization, and schema evolution. Proven track record harmonizing data across multiple source systems with conflicting schemas, identifiers, or grain. BI tools (DOMO, Looker, Mode) — comfort both consuming and supporting their development. Experience debugging data-quality issues across the full stack — from BI tool to warehouse to raw event logs. BA/BS in Computer Science, Math, Physics, Engineering, Economics, Statistics, or a related quantitative field. Strong analytical, logical, and communication skills. Preferred Qualifications Semantic-layer experience (LookML, dbt Semantic Layer, Cube). Streaming-media / ad-tech domain (ad servers, SSAI / stitchers, VAST/VMAP, impression and fill-rate metrics). Streaming ingestion (Kafka, Pub/Sub, Kinesis). Statistical analysis with R and predictive-analytics tooling; experimentation / A-B analysis. Master data management or entity resolution. MS in a quantitative discipline; experience mentoring or leading engineers/analysts. How you will succeed in this role: You think in models and metrics, not just pipelines. You are the person others come to when they are lost in the data — and you like being that guide. You are as comfortable in a SQL console or a dashboard as in a DAG, and you move fluidly between business context and technical root cause. You treat data as a product — with users, definitions, SLAs, and documentation. You turn ambiguous business questions into clean models and clear answers. Paramount Streaming, a division within Paramount Global, is the home to the company's direct-to-consumer services spanning free and paid in the form of Pluto TV and Paramount+. Pluto TV is the global leader in free ad-supported TV, delivering more than 1,400 global channels and an extensive library of streaming content, including live and original channels. Paramount+, digital subscription video-on-demand and live streaming service, combines live sports, breaking news, and A Mountain of Entertainment™. Paramount+ features an expansive library of original series, hit shows and popular movies across every genre from world-renowned brands and production studios, including SHOWTIME®. Additional Information Hiring Salary Range: $110,000.00 - 165,000.00. The hiring salary range for this position applies to New York, California, Colorado, Washington state, and most other geographies. Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education. The benefits available for this position include medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program and PTO or, if applicable, as otherwise dictated by the appropriate Collective Bargaining Agreement. This position is bonus eligible. What We Offer: Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here: https://www.paramount.com/careers/benefits Generous paid time off. An exciting and fulfilling opportunity to be part of one of Paramount's most dynamic teams. Opportunities for both on-site and virtual engagement events. Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace. Explore life at Paramount: https://www.paramount.com/careers/life-at-paramount Paramount is an equal opportunity employer (EOE) including disability/vet. At Paramount, the spirit of inclusion feeds into everything that we do, on-screen and off. From the programming and movies we create to employee benefits/programs and social impact outreach initiatives, we believe that opportunity, access, resources and rewards should be available to and for the benefit of all. Paramount is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, and Veteran status. If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access https://www.paramount.com/careers as a result of your disability. You can request reasonable accommodations by calling 212.846.5500 or by sending an email to paramountaccommodations@paramount.com. Only messages left for this purpose will be returned.

THE BUSINESS We are Everyday Luxury™. It's what we do and how we do it — together. It's working alongside world-class talent in pursuit of world-class results. It's maximizing your potential in a high-performance culture. It's knowing that wherever you are, you belong. It's elevating your world — and the world of those who love and will love Aritzia. It's You. And an endless runway of opportunity. With a Global Support Campus in Vancouver and additional Support Office hubs in major cities across North America — including Toronto, New York, Los Angeles and Seattle — our workplace network is strategically designed to support our People and serve our Clients a frictionless experience. So, you can do what you do best — with us. THE TEAM The mission of the Digital Technology department is to design, build, deliver and operate robust, efficient, and scalable technology solutions that align with our strategic, business and technology goals. THE OPPORTUNITY Aritzia is growing and our Digital Technology team is growing with it. This is a unique opportunity to be part of the team responsible for delivering everyday luxury experiences to Aritzia's clients by leveraging technology to meet and exceed the vision and mission of the business. As the Senior Analytics Engineer / Analytics Engineer, you will transform complex data into trusted, business-ready assets that empower decision-making across the organization. You will partner closely with cross-functional stakeholders to establish consistent metrics, develop scalable data models, and improve the accessibility and reliability of data. By combining strong technical capabilities with business acumen, you will create the foundation for meaningful insights, high-quality reporting, and data products that unlock opportunities, drive performance, and enable confident decision-making. And, with the skills you gain in this role, the opportunities are endless — from a rewarding career in Digital Technology to continued growth and development with Aritzia. THE ROLE As a Senior Analytics Engineer / Analytics Engineer, Digital Technology, you will: Design, develop, and optimize trusted, reusable data models that support analytics, reporting, and downstream data products Transform raw landed data into curated, documented datasets that enable consistent business use Partner closely with analytics, data science, engineering, and business stakeholders to translate business needs into scalable analytical assets Define and maintain semantic layers that support business intelligence, self-serve analytics, and downstream consumption Establish and uphold data quality, governance, privacy, and documentation standards for trusted analytical datasets Contribute to the evolution of the customer data model, improving usability, consistency, and reliability across data products Support the development of data testing, lineage, and documentation practices that increase trust in business reporting Mentor junior engineers and promote analytics engineering best practices, data standards, and operational excellence THE QUALIFICATIONS The Senior Analytics Engineer / Analytics Engineer, Digital Technology has: Proven skills, education, and/or applicable certifications in computer science, analytics engineering, business intelligence, and/or data analytics Proficiency in SQL and data modeling for analytics, reporting, and downstream data products Experience developing transformation workflows and documented datasets using modern analytics engineering tools, with dbt preferred Experience with modern data warehousing solutions (BigQuery, Snowflake, Redshift preferred) Experience defining metrics, business logic, semantic layers, and reusable data products Experience with data quality, testing, governance, lineage, and documentation standards Experience supporting Digital Marketing (DTC, Owned, Paid channels) and Customer Data (zero/first/third-party) preferred Curiosity to leverage AI to accelerate workflows and generate business solutions A commitment to learn and apply Aritzia's Values, Business and People Leadership principles The ability to collaborate fluently with cross-functional partners A commitment to quality and investing in results that add value to the business At Aritzia, we provide market-leading compensation packages designed to reward impact through our pay-for-performance philosophy. The range for roles at this level is $100,000–$300,000 per year. Total compensation may include base, bonus and/or equity depending on the role, which has the potential to be greater than this range. With comprehensive benefits, aspirational workspaces, and elevated perks and experiences — we provide it all. THE PERKS Some of the industry-leading benefits you will receive working at Aritzia: Product Discount — Maybe you've heard of our famous product discount? Or our exclusive private shopping events? You have now. Baby Leave Program — We provide up to 16 weeks of paid time to support you and your growing family. Aritzia Virtual Wellness — Because your health, happiness and safety matter, we provide 24/7 resources to support you in your wellbeing goals, whether they're physical, mental, social or financial. Aspirational Workplace — Our offices are specially designed to be spaces of creativity, productivity and inspiration. Amenities — Additional amenities include a private parent's room, shower facilities with elevated complementary conveniences, bike rooms and more. The Extras — We also offer a multitude of other perks like snacks and refreshments, dry-cleaning, hotel, restaurant discounts, health and wellness discounts, and self-care promos.

Who We Are At Moxxi, we're redefining performance marketing with cutting-edge technology and a relentless focus on results. As a profitable, bootstrapped start-up, we are led by a team of industry veterans in performance marketing, ad tech, and digital publishing. Together, we're building the premier platform to incubate, scale, and optimize digitally born businesses, setting new standards for data-driven success. Our entrepreneurial team brings proven experience from both global corporations and fast-growing startups. We're passionate about delivering scalable, profitable outcomes for our partners through advanced profiling, targeting, and continuous optimization. Joining Moxxi means becoming part of a growing start-up in the heart of NYC, where you'll have the opportunity to accelerate your career, expand your skill set, and shape the future of performance marketing. Location: New York City Level: Mid-level, 3 to 5 years experience The Opportunity As an Analytics Engineer at Moxxi, you will build and own the data models behind our marketplace reporting, and you will use them to answer questions the business has not yet figured out how to ask. This is a small analytics team, so you will not be handed a queue of tickets. You will work directly with account managers, yield, and product to understand what is actually happening in the business, then build the models and reporting that make those answers repeatable. A significant part of this role is applying LLMs to analytics work: connecting models to the warehouse, engineering the context they need to be useful, and building the evals that tell you whether the output can be trusted. Our Analytics Lead owns this area, and you will be a key contributor to it. Moxxi owns its ad server, CMS, user journey orchestration, and SMS platform, so we generate our own data and can change the systems that produce it. That also means analytics here covers both sides of the business. You will report on marketplace performance, and you will measure what product ships: how a change to a user journey affects conversion, whether a new feature did what we expected, and what that tells us to build next. You will be joining shortly after a platform migration, so expect to help define and instrument the events behind that work rather than inherit a mature measurement stack. Over time, we expect this role to grow into quantitative work on how we model and select offers. Responsibilities Own marketplace reporting across publishers, advertisers, and verticals, and make sure the numbers the business runs on are correct. Build and maintain dbt models in the warehouse, with tests, in version control, shipped to production. Investigate anomalies end to end: what moved, why it moved, and what we should do about it. Apply LLMs to analytics workflows, including context engineering and evaluation, in partnership with the Analytics Lead. Measure product work: define success metrics for new features and journey changes, specify the events needed to track them, and report back on whether the change did what we expected. Partner with operators to define KPIs so the business argues about decisions rather than about whose number is right. Build self-service reporting so routine questions stop routing through the analytics team. Own data quality: reconciliation across sources, and validation that catches problems before consumers do. Support experimentation and measurement design as we roll out platform and product changes. Grow into quantitative optimization work, including improving how we model and select what offers and experiences we serve to each user session. Basic Qualifications 3 to 5 years of experience in analytics, analytics engineering, or data science. Hands-on experience building with LLMs against real data, including how you validated whether the output was trustworthy. Demos and prompt experimentation are not sufficient. Production experience with dbt: you have owned models, written tests, worked in Git, and shipped. Advanced SQL and working proficiency in Python. Experience with a cloud data warehouse (BigQuery preferred, Snowflake or Redshift acceptable). Practical experience with data quality work: reconciliation across sources, and validating your own output. The ability to hold a business conversation. You should be able to walk us through a question you chased end to end, what you thought was happening, what you found, and what changed as a result. Comfort with ambiguity and a track record of deciding what to work on rather than waiting to be told. Nice to Have / Bonus Qualifications Experience in AdTech, performance marketing, or marketplace businesses. Experience as the first or only data person at a company. Product analytics experience: success metrics, event instrumentation, and measuring feature impact. Experimentation design and applied statistics. Exposure to bid optimization, yield management, or similar decision-modeling work. Familiarity with monitoring and observability tooling such as Datadog or Grafana. Moxxi Values & Competencies Self-Starter – You take initiative and solve problems before they become problems. Curious & Analytical – You ask why, dig deep into data, and experiment to learn what works. Efficiency-Oriented – Fast, focused, and action-driven. Relationship-Focused – You build trust and deliver value. Organized Multitasker – You thrive in fast-paced, high-velocity environments. Strategic Communicator – You translate performance into insight and insight into action. Salary Range: $120,000 USD - $140,000 USD

Overview Working at Atlassian Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. We're looking for a versatile, seasoned sales data analyst or analytics engineer that will focus on driving data alignment (improving business processes and production systems) between enterprise go-to-market and finance. You'll design clean, robust data pipelines and infrastructure to monitor data drift between systems/applications like Salesforce, Oracle Fusion, Gainsight, and Databricks. As an experienced senior analytics engineer on our team, you'll help evolve our data stack, champion best practices in data processing + analytics design, and build a robust context layer on top of our data that enables AI agents to interact safely. Responsibilities Design, build, and maintain data infrastructure that ensures data quality, consistency, and accessibility for analytical workflows Lead the development of data products and an AI context layer that enables analytics and data access to scale across the company Build and maintain canonical datasets by developing high-volume, reliable ETL/ELT pipelines using data lake and data warehousing concepts Partner with finance, data engineering, and other x-functional teams to track and flag data inconsistencies that obscure the company's ability to accurately close our books Execute sizing analyses for transformational and ad-hoc data infrastructure changes, including well-defined impact analyses to support new and ongoing initiatives and product launches, conducting investigations and QA on source data quality Develop SME-level knowledge and ownership for core system data transformations (owning the requirements → UAT → DE implementation cycle for a new product or data structure) Leverage available tools to find efficiencies and process improvements to help us and our partner teams (deal desk, central analytics) remove manual processes and improve source data accuracy Investigate reported bugs or deficiencies, working with business analysts and data engineering to do UAT/QA on simple bug-fix & feature releases Compensation At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range. Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter. This role may also be eligible for benefits, bonuses, commissions, and equity. Pay Ranges Role In The United States , we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are: Zone A : $156,600 - $204,450 Zone B : $140,940 - $184,005 Zone C : $129,978 - $169,694 Qualifications 6+ years of experience in Analytics Engineering, Data Engineering, Data Science, Business Intelligence, or similar field. Expert level proficiency with SQL and data modeling. You know how to architect data models and influence engineering teams to build for scale and consistency Proficiency in a quantitative programming language, preferably Python Experience with BI tools such as Tableau, Looker, and know how to enable self-service analytics Experience with CRM systems like Salesforce or Hubspot and ERP systems like Oracle or Netsuite Understand effective software and analytics engineering practices and can use AI tools to move faster without creating brittle systems, unclear code, or unnecessary complexity. Ability to independently structure complex, ambiguous problems from initial framing through delivery and execution with diverse teams up to the senior leadership level Proactive engagement with the management team to identify, prioritize, develop, and execute strategies that improve our data to enable better decisionmaking Comfortable collaborating across numerous horizontal partners (sales systems, sales enablement, sales compensation) as well as departmental/vertical stakeholders (marketing, product, finance) to build alignment and deliver results Excellent problem-solving skills with a focus on delivering business impact through reliable systems Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together. We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines. To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them. To learn more about our culture and hiring process, visit go.atlassian.com/crh . In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.

Analytics Engineer About Sprinter Health At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can't get to a doctor's office. For many, the ER becomes their first touchpoint with the healthcare system, driving over $300B in avoidable costs every year. By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we've supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators has raised over $125M from investors like a16z, General Catalyst, GV, and Accel and enjoys multi-year runway. About The Role We're looking for an Analytics Engineer to build the trusted data layer that analysts, data scientists, operations, finance, product, and our payer customers depend on. At Sprinter, data is central to how we operate, measure performance, serve patients, and support our health plan partners. This role will own the canonical models, metric definitions, transformation logic, documentation, and tests that make our data reliable and reusable across the company. You'll help define what each table, field, and metric means, then build the infrastructure that ensures those definitions are consistently applied. That includes modeling data in dbt or equivalent tooling, creating reporting-ready tables, improving lineage and documentation, reconciling metrics across teams, and helping prevent the kind of data drift and metric chaos that slows companies down as they scale. This role is ideal for someone who treats metric definitions as product artifacts, thinks in contracts and tests, and cares deeply about making data trustworthy for both internal users and external customers. Office Location We are a hybrid company based in the Bay Area with offices in both San Francisco and Menlo Park. We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work. What you will do Build canonical data models that create a shared source of truth across the company Define and maintain core business, operational, financial, product, and customer-facing metrics Model data in dbt or equivalent transformation tooling so dashboards, self-serve analytics, and customer reports pull from trusted tables Write tests, documentation, and data quality checks that catch issues before they reach users Create clear definitions for tables, fields, and metrics so teams understand what the data means and when to use it Reconcile metric definitions across internal teams, external reporting needs, and payer customer expectations Trace data lineage and debug dashboards, reports, or tables that change unexpectedly Partner with analysts, data scientists, operations, finance, product, engineering, and customer-facing teams to understand data needs and translate them into reliable models Help build reusable reporting frameworks that make onboarding new payers faster and less manual Partner with the data platform team to evolve warehouse tables, improve data architecture, and strengthen data contracts Improve warehouse cost, performance, and maintainability Support PHI-aware data access patterns and help ensure sensitive healthcare data is modeled and used responsibly What you have done Built analytics engineering, business intelligence, or data modeling systems in a production cloud warehouse environment Written expert-level SQL and designed data models that support reporting, analysis, and decision-making Worked with dbt or an equivalent transformation framework Built tested, documented, reusable data models rather than one-off queries Defined, maintained, or reconciled business-critical metrics across teams Partnered with analysts, data scientists, operators, finance teams, product teams, or customer-facing stakeholders Debugged data quality issues, dashboard changes, metric discrepancies, and lineage problems Worked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similar Balanced speed, correctness, usability, and maintainability when building data assets Communicated clearly with technical and non-technical stakeholders about what data means and how it should be used What gives you an edge You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets You've worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated data You have experience building customer-facing reporting, embedded analytics, or multi-tenant data models You've worked with row-level security, access controls, or governed self-serve analytics You have experience using Python for analysis, scripting, data validation, or automation You've helped establish a semantic layer, metrics layer, or company-wide source of truth You've built data models in a high-growth startup or operationally complex environment You have experience improving warehouse performance, cost, and query efficiency What makes you successful You treat a metric definition as a product artifact, not a Slack thread You make data trustworthy, reusable, and easy to understand You prevent metric chaos by building clear definitions, tests, and documentation You build so that a fix in one place does not require five copy-paste edits elsewhere You understand that internal users and external customers both need data they can trust You care about the usability of the data model, not just whether the pipeline runs You can explain data discrepancies clearly and drive teams toward shared definitions You build foundations that help the company move faster with more confidence Day to Day In This Role, You Might Spend Your Time Building or refactoring dbt models Adding tests to core tables Defining canonical fields and documenting how they should be used Reviewing metric definitions and reconciling them across teams Debugging a dashboard, report, or customer-facing metric that changed unexpectedly Tracing lineage from source systems through warehouse models to downstream reports Partnering with analysts, operators, finance, product, or customer-facing teams on reporting needs Improving warehouse performance, cost, and maintainability Designing reusable reporting structures that make new payer launches easier The Interview Process We aim to complete the interview process within 2–3 weeks. It will usually consist of: Recruiter Screen: Background fit, motivation, and compensation alignment Hiring Manager Interview: Analytics engineering experience, data modeling depth, and stakeholder partnership Hands-on Technical Assessment: SQL, data modeling, metric design, and practical analytics engineering judgment Onsite Interview: Technical case study, systems/data modeling discussion, behavioral interview, and lunch with the team References: Validation of performance, judgment, and working style What we offer Meaningful pre-IPO equity Medical, dental, and vision plans 100% paid for you and your dependents Flexible PTO + 10 paid holidays per year 401(k) with match 16-week parental leave policy for birthing parent, 8 weeks for all other parents HSA + FSA contributions Life insurance, plus short and long-term disability coverage Free daily lunch in-office Annual learning stipend Relocation assistance Equal Opportunity Statement Sprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected classes. Beware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers. If you are applying for a job, you can confirm the legitimacy of a job posting by viewing current open roles on our official Sprinter Health Careers website. All legitimate job postings will require an application to be made directly on our official Sprinter Health Careers website. Job-related communications will only be sent from email addresses ending in @sprinterhealth.com. Please ensure that you're only replying to emails that end with @sprinterhealth.com.