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About Us Think BIG. Achieve More. At City National Bank of Florida, we invest in our people and the communities we serve. Join a team where ambitious careers and meaningful relationships thrive together. We're entrepreneurs at heart—growing fast, making an impact, and shaping the future of banking in Florida. To learn more about City National Bank of Florida visit About Us! Overview City National Bank of Florida is seeking a hands-on Machine Learning / AI Engineer to develop enterprise-grade AI applications, retrieval-augmented generation (RAG) systems, and agentic AI workflows. The engineer will work on real banking use cases involving financial document analysis, credit writing, enterprise knowledge retrieval, risk and fraud prevention, and employee productivity. The ideal candidate combines strong Python software engineering skills with practical experience building, testing, and deploying LLM-powered applications. This is a development-focused role, not a pure research or experimentation position. What You'll Do Design, develop, test, and maintain production-quality Python applications for generative AI and applied machine learning. Build RAG pipelines using document ingestion, parsing, chunking, embeddings, metadata filtering, hybrid search, reranking, and grounded response generation. Develop AI agents and workflow components using LangChain v1 and LangGraph. Integrate Azure OpenAI models and Azure AI Search into enterprise AI applications. Implement structured outputs using Pydantic, schema validation, deterministic business rules, and robust error handling. Build document validation, financial data extraction, summarization, classification, and analysis components. Implement automated evaluation datasets and tests to measure retrieval quality, answer correctness, grounding, and regression. Develop REST APIs and backend services using FastAPI or an equivalent Python framework. Write unit tests, integration tests, and mock-based tests; participate in code reviews and continuous integration. Implement secure handling of sensitive banking information, prompt-injection defenses, input validation, and appropriate AI guardrails. Deploy containerized applications and contribute to Azure monitoring, logging, troubleshooting, and performance optimization. Collaborate with senior engineers, AI leadership, business stakeholders, and risk and compliance teams. Qualifications Programming and software engineering 4–5 years of professional software development experience, with strong hands-on Python proficiency. Strong knowledge of object-oriented programming, data structures, algorithms, modular design, and clean-code principles. Experience with FastAPI, REST APIs, Pydantic, SQL, Git, pytest, and debugging. Generative AI and machine learning Practical experience building LLM-powered applications using OpenAI, Azure OpenAI, or comparable model APIs. Understanding of transformers, embeddings, tokenization, semantic search, and model inference. Hands-on experience with RAG, including document ingestion, chunking strategies, retrieval, grounding, and evaluation. Experience with LangChain, LangGraph, or comparable orchestration frameworks. Direct LangGraph experience is strongly preferred. Experience with prompt design, structured generation, function/tool calling, and output validation. Ability to measure and improve AI quality rather than relying solely on subjective demonstrations. Cloud and engineering practices Experience with Azure or another major cloud platform; Azure experience is preferred. Familiarity with Docker, CI/CD, application logging, monitoring, and automated testing. Ability to troubleshoot API failures, retrieval defects, latency, token usage, and model-related errors. Preferred Qualifications Experience with Azure AI Search, hybrid search, reranking, or vector databases. Experience building AI agents that call enterprise APIs and tools. Familiarity with Model Context Protocol (MCP). Experience with PostgreSQL, Azure Blob Storage, or document-processing pipelines. Experience in banking, financial services, lending, fraud, or another regulated industry. Familiarity with responsible AI, data privacy, and auditability. Education Bachelor's Degree in computer science, data science, engineering, applied mathematics/statistics, information systems, or a related quantitative field (or equivalent combination of education and experience). Master's Degree preferred. Benefits & Perks We offer a competitive benefits package designed to support your success: Health Coverage: Medical, dental, and vision plans with employer contributions. Retirement Savings: 401(k) with matching. Paid Time Off & Holidays: Generous PTO and paid holidays. Wellness Support: Access to mental health and financial wellness resources. Visit our benefits page to learn more Benefits! Join City National Bank of Florida. The possibilities are endless. Special Information To Candidates Equal Opportunity City National Bank of Florida is an Equal Opportunity Employer. We do not discriminate based on race, color, religion, sex, national origin, age, disability, genetic information, protected veteran status, or any status protected by federal, state, or Florida law. We comply with the ADA and applicable Florida laws. Interview Guidelines To ensure a fair and consistent interview process, applicants are not permitted to use AI-enabled devices during interviews, including AI glasses or similar wearable technology that can record, transmit, or generate real-time information. Applicants who require a reasonable accommodation to participate in the interview process should contact talent.attraction@citynational.com in advance of the interview to submit an accommodation request. City National Bank of Florida will engage in the interactive process and provide reasonable accommodations in accordance with applicable law.

See yourself at Dataminr As a Senior AI Engineer at Dataminr, you'll work alongside Research Scientists and Data Scientists to train, adapt, and deploy state-of-the-art LLMs and agents across Predictive, Generative, and Agentic AI. You'll tackle multimodal data, including text, images, video, audio, and sensor data, on a platform that processes billions of inputs every day, helping organizations act on critical events as they unfold. Expect plenty of room to keep growing in both AI and software engineering. This is a hybrid role with two in-office days each week in our New York City or Arlington, VA office. AI Innovation at Dataminr Working at Dataminr you'll have the opportunity to tackle the most exciting trends in AI on a daily basis to power a revolutionary product that uncovers critical events around the world as they unfold. Regenerative AI: our AI technology, ReGenAI, is a new form of generative AI that automatically regenerates real-time Live Event Briefs as events unfold. Learn more here. Agentic AI: we recently launched our Agentic AI capability, what we're calling our Intel Agents, that autonomously generates critical context for our clients on real-time events, threats, and risks allowing them to see the clearest, most accurate view of what's happening on the ground. Learn more here Multimodal AI: our platform detects events from many different types of data (images, video, sensor data, audio, and text in over 150 languages). Learn more here. The opportunity Work with groups of Research Scientists, Engineers, and Data Scientists in the AI Platform organization and beyond, to deploy, build, adapt, optimize, and deploy LLM models in production at very large scale. Own the technical design and implementation of robust API services, libraries, and container strategies that define and support our end-to-end LLMOps lifecycle. Optimize DL/LLM training and inference performance (speed/cost) on specialized hardware. Drive the full lifecycle of complex AI problems: from initial research and literature review to hands-on data exploration, implementation of novel, in-house methods, and successful productization. What You Bring At Dataminr, we value you for who you are. We encourage you to apply for this role, even if you don't meet every qualification. Our candidates are reviewed on the basis of their skill and potential to succeed. M.S. in Computer science or equivalent experience 5+ years of experience as a Research or Machine Learning Engineer. MLOps/LLMOps Experience: building, optimizing, deploying and operating Deep Learning models using PyTorch, including Transformers and LLMs, not just using APIs. Experience working with Research Scientists, reading technical research papers, and implementing state-of-the-art methods in Natural Language Processing, Computer Vision, Knowledge Representation and Management, Vector Databases and Generative AI. Expertise in Python. Experience with Containers, Kubernetes, AWS, Databricks, and Agentic infra such as LangGraph, LangFuse, and vector stores About Dataminr At Dataminr, we are a mission driven team of talented builders, creators and visionaries who have real-world impact on how organizations are able to respond to events. Dataminr's groundbreaking, AI-powered, intelligence platform provides organizations with the earliest signals of emerging risks, events, and threats before they unfold. Trusted by two-thirds of the Fortune 50 and half of the Fortune 100, Dataminr's platform analyzes billions of public data inputs spanning text, image, video, audio and sensor data across 150+ languages, empowering our clients to stay one step ahead in an increasingly complex world where every second counts. Founded in 2009, we have pioneered the world's first real-time event detection platform, long before the recent Gen AI 'boom.' Dataminr operates all around the world united by our passion to use AI for the greater good, be agents of positive change and put our technology into the hands of clients charged with the responsibility to keep organizations running and keep people safe. As our employees focus on developing our revolutionary technology, we focus on our employees. Dataminr is proud to offer a variety of flexible work arrangements, offices all over the world to foster collaboration, generous PTO and sick leave, and more, as part of our competitive benefits package aimed at keeping all our employees happy and healthy. Explore all our benefits here . We believe our differences give us strength. Our employees are empowered to be their best, authentic selves through various opportunities, such as our robust employee resource group (ERG) network, manager development programming, professional development funds, and more. We serve a global community made up of many cultures and strive to reflect the world and clients we serve, with a workforce built on merit and equity. We actively condemn racism and discrimination in any form. We stand for social good, fostering a culture of allyship, and standing up for those who face systemic barriers to equality. We lead with empathy and strive to be agents of positive change in our company and in our communities. The annual base salary range for this position is $154,800 - $227,500. You will also be eligible to receive a discretionary bonus and Company equity. Actual salary will be based on a number of factors including, but not limited to, geographic location, applicant skills, and prior relevant experience. Dataminr is an equal opportunity and affirmative action employer. Individuals seeking employment at Dataminr are considered without regards to race, sex, color, creed, religion, national origin, age, disability, genetics, marital status, pregnancy, unemployment status, sexual orientation, citizenship status or veteran status. Dataminr will collect and process your personal data in accordance with Dataminr's candidate privacy notice available here . By providing your details and applying via our careers website, you acknowledge that you have read our candidate privacy notice. If you have any queries, please contact recruiting@dataminr.com or privacy@dataminr.com . Notice Regarding Automated Employment Decision Tools in New York City

About The Role The role designs and ships production LLM and GenAI systems: retrieval-augmented generation pipelines, agentic workflows, fine-tuning jobs, and evaluation harnesses that hold up under real enterprise traffic. This is not a prompt-tinkering position. The team builds the core AI platform — model serving infrastructure, evaluation frameworks, and RAG architecture — and this role owns meaningful pieces of it end-to-end, working alongside applied scientists, backend engineers, and product leadership. Key Responsibilities Architect and build RAG pipelines using LangChain, LlamaIndex, or custom retrieval stacks, tuning chunking strategies, hybrid search, and reranking for measurable quality gains Implement and scale vector database integrations (Pinecone, Weaviate, pgvector) serving low-latency semantic search in production Design agentic workflows with tool calling, function execution, and guardrails using frameworks like LangGraph or custom orchestration layers Run fine-tuning workloads (LoRA, QLoRA, DPO) on domain-specific datasets using PyTorch and Hugging Face, managing GPU resources across training runs Build systematic LLM evaluation frameworks: golden datasets, LLM-as-judge pipelines, and regression suites wired into CI so quality regressions are caught before deploy Deploy and serve models via vLLM, Triton, or managed inference endpoints, optimizing for cost, latency, and throughput Own observability for GenAI features: tracing with LangSmith or OpenTelemetry, drift detection, and dashboards that surface failure modes quickly What We Are Looking For 3–6 years of software engineering or ML engineering experience, with at least 2 years shipping LLM-powered systems to production Strong Python engineering skills: async programming, API design, testing, and working comfortably in large codebases Hands-on production experience with RAG architecture, including embedding model selection, vector databases, and retrieval quality tuning Experience fine-tuning or evaluating open-source models (Llama, Mistral, Qwen) and working with provider APIs (OpenAI, Anthropic, Bedrock) Working knowledge of cloud infrastructure (AWS, GCP, or Azure), containerized deployments, and GPU-based serving Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience Bonus: experience with RLHF or preference optimization, contributions to open-source LLM tooling, multimodal model experience, or an MS/PhD in a relevant field

Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd®, Slideshare®, Everand™, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise. Culture at Scribd, Inc. We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. We believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture. Occasional in-person attendance is required for all Scribd, Inc. employees, regardless of location. So what are we looking for in new team members? At Scribd, Inc., we hire for "GRIT." Traditionally defined as the intersection of passion and perseverance toward long-term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Innovative ideas and solutions, and strengthening the broader Team through collaboration and attitude. This posting reflects an approved, open position within the organization. About The Team Premium is the Scribd Inc. pillar behind two reading products: Everand, the audiobook and ebook subscription app, and Fable, the social app where readers track, discuss and discover books. Helping readers find their next book is central to both, so search and recommendations sit at the heart of the products. You'll join Premium's new Discovery squad. It's a small, dedicated group of recommendations, search, applied research and ML engineers working full time on discovery for Everand and Fable. The squad works closely with Orion, Scribd's ML platform team. Orion runs the feature store, model registry, embedding-based retrieval and model serving behind discovery across Scribd, Slideshare and Everand. Role Overview We're looking for a Senior Machine Learning Engineer to own recommendations for Fable and grow into the technical lead for Premium recommendations. Fable's recommender today is a collaborative-filtering model built in house. It has served readers well, but it has no real-time signals and no learned ranking, and it can't recommend to new or lapsed readers. Your first big project is to move Fable onto Orion, so Fable and Everand share a modern recommendation stack. Key Responsibilities Lead the migration of Fable's recommendations onto Orion, covering data, features, training, serving and experiments, while keeping today's recommendations running. Extend Orion's rankers, feature store and embedding-based retrieval to support Fable, in partnership with the Orion ML platform team. Own the health of Fable's recommendation systems in production: quality, latency, cost and on-call. Bring Fable's social signals (shelves, ratings, reviews, book clubs, reader similarity) into recommendations for both Fable and Everand. Design and analyze A/B tests with product managers and analysts to decide what ships. Act as technical lead for Premium recommendations. Shape the roadmap with product managers, and see work that depends on other teams' systems through to delivery. Raise the bar on engineering practice in the squad through design reviews, code quality and operational readiness. Tech you'll work with You'll work across two stacks: Fable's on Google Cloud and Orion's on AWS. Fable: Python (FastAPI, Django), PostgreSQL, BigQuery, Kubernetes, Google Cloud including Vertex AI Orion and Everand: Python, Go and gRPC, Ruby on Rails, Databricks and Spark, Airflow, AWS SageMaker, Weaviate for embedding-based retrieval, feature store and model registry Infrastructure: AWS (EKS, ECS, Lambda, SQS, ElastiCache), Datadog, Terraform Requirements Must have 5+ years as a professional ML or software engineer, with a track record of shipping production ML systems. Hands-on experience building and running recommender systems in production, such as collaborative filtering or matrix factorization, two-tower or embedding retrieval, and learned ranking. Experience with large-scale data processing (Spark, Databricks, BigQuery or similar). Experience on at least one major cloud (AWS or GCP), including deploying services on containers (Kubernetes, ECS or similar). Experience designing and analyzing online experiments. A record of leading projects that depend on other teams, and of working directly with product managers. Bachelor's or Master's degree in Computer Science, or equivalent professional experience. Nice to have Google Cloud experience, especially Vertex AI and BigQuery. Experience migrating or replacing a production ML system while keeping it running. Social or graph-based recommendations, such as user similarity, follows or groups. Search and semantic retrieval (Elasticsearch, Vertex AI Search, vector databases). Experience on consumer apps for reading, media or social. At Scribd, Inc. your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States. In the state of California, the reasonably expected salary range is between $170,000 [minimum salary in our lowest geographic market within California] to $265,000 [maximum salary in our highest geographic market within California]. In the United States, outside of California, the reasonably expected salary range is between $140,000 [minimum salary in our lowest US geographic market outside of California] to $252,000 [maximum salary in our highest US geographic market outside of California]. In Canada, the reasonably expected salary range is between 179,000 CAD[minimum salary in our lowest geographic market] to 237,500 CAD[maximum salary in our highest geographic market]. We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package. Working at Scribd, Inc. Are you currently based in a location where Scribd, Inc. can employ you? Employees must have their primary residence in or near one of the following cities. This includes surrounding metro areas or locations within a typical commuting distance: United States Atlanta | Austin | Boston | Dallas | Denver | Chicago | Houston | Jacksonville | Los Angeles | Miami | New York City | Phoenix | Portland | Sacramento | Salt Lake City | San Diego | San Francisco | Seattle | Washington D.C. Canada Ottawa | Toronto | Vancouver Mexico Mexico City Benefits At Scribd, Inc. Scribd Flex (flexible work model) Comprehensive health, dental, and vision coverage Mental health support and disability coverage Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals Paid parental leave and family support benefits Retirement matching and employee equity Learning and development programs and professional growth opportunities Wellness and home office stipends Complimentary access to the Scribd, Inc. suite of products Enterprise access to leading AI tools Get to Know Scribd, Inc. About Scribd, Inc. Life at Scribd, Inc. We want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make to better accommodate your needs by emailing accommodations@scribd.com about the need for adjustments at any point in the interview process. If you apply for a job with Scribd, Inc. or otherwise engage with us in connection with employment (including as an employee, contractor, or other personnel), the personal information we process in that context is subject to our Employee and Applicant Privacy Policy, which is available here . Scribd, Inc. is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.

Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd®, Slideshare®, Everand™, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise. Culture at Scribd, Inc. We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. We believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture. Occasional in-person attendance is required for all Scribd, Inc. employees, regardless of location. So what are we looking for in new team members? At Scribd, Inc., we hire for "GRIT." Traditionally defined as the intersection of passion and perseverance toward long-term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Innovative ideas and solutions, and strengthening the broader Team through collaboration and attitude. This posting reflects an approved, open position within the organization. About The Team Premium is the Scribd Inc. pillar behind two reading products: Everand, the audiobook and ebook subscription app, and Fable, the social app where readers track, discuss and discover books. Helping readers find their next book is central to both, so search and recommendations sit at the heart of the products. You'll join Premium's new Discovery squad. It's a small, dedicated group of recommendations, search, applied research and ML engineers working full time on discovery for Everand and Fable. The squad works closely with Orion, Scribd's ML platform team. Orion runs the feature store, model registry, embedding-based retrieval and model serving behind discovery across Scribd, Slideshare and Everand. Role Overview We're looking for a Senior Machine Learning Engineer to own recommendations for Fable and grow into the technical lead for Premium recommendations. Fable's recommender today is a collaborative-filtering model built in house. It has served readers well, but it has no real-time signals and no learned ranking, and it can't recommend to new or lapsed readers. Your first big project is to move Fable onto Orion, so Fable and Everand share a modern recommendation stack. Key Responsibilities Lead the migration of Fable's recommendations onto Orion, covering data, features, training, serving and experiments, while keeping today's recommendations running. Extend Orion's rankers, feature store and embedding-based retrieval to support Fable, in partnership with the Orion ML platform team. Own the health of Fable's recommendation systems in production: quality, latency, cost and on-call. Bring Fable's social signals (shelves, ratings, reviews, book clubs, reader similarity) into recommendations for both Fable and Everand. Design and analyze A/B tests with product managers and analysts to decide what ships. Act as technical lead for Premium recommendations. Shape the roadmap with product managers, and see work that depends on other teams' systems through to delivery. Raise the bar on engineering practice in the squad through design reviews, code quality and operational readiness. Tech you'll work with You'll work across two stacks: Fable's on Google Cloud and Orion's on AWS. Fable: Python (FastAPI, Django), PostgreSQL, BigQuery, Kubernetes, Google Cloud including Vertex AI Orion and Everand: Python, Go and gRPC, Ruby on Rails, Databricks and Spark, Airflow, AWS SageMaker, Weaviate for embedding-based retrieval, feature store and model registry Infrastructure: AWS (EKS, ECS, Lambda, SQS, ElastiCache), Datadog, Terraform Requirements Must have 5+ years as a professional ML or software engineer, with a track record of shipping production ML systems. Hands-on experience building and running recommender systems in production, such as collaborative filtering or matrix factorization, two-tower or embedding retrieval, and learned ranking. Experience with large-scale data processing (Spark, Databricks, BigQuery or similar). Experience on at least one major cloud (AWS or GCP), including deploying services on containers (Kubernetes, ECS or similar). Experience designing and analyzing online experiments. A record of leading projects that depend on other teams, and of working directly with product managers. Bachelor's or Master's degree in Computer Science, or equivalent professional experience. Nice to have Google Cloud experience, especially Vertex AI and BigQuery. Experience migrating or replacing a production ML system while keeping it running. Social or graph-based recommendations, such as user similarity, follows or groups. Search and semantic retrieval (Elasticsearch, Vertex AI Search, vector databases). Experience on consumer apps for reading, media or social. At Scribd, Inc. your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States. In the state of California, the reasonably expected salary range is between $170,000 [minimum salary in our lowest geographic market within California] to $265,000 [maximum salary in our highest geographic market within California]. In the United States, outside of California, the reasonably expected salary range is between $140,000 [minimum salary in our lowest US geographic market outside of California] to $252,000 [maximum salary in our highest US geographic market outside of California]. In Canada, the reasonably expected salary range is between 179,000 CAD[minimum salary in our lowest geographic market] to 237,500 CAD[maximum salary in our highest geographic market]. We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package. Working at Scribd, Inc. Are you currently based in a location where Scribd, Inc. can employ you? Employees must have their primary residence in or near one of the following cities. This includes surrounding metro areas or locations within a typical commuting distance: United States Atlanta | Austin | Boston | Dallas | Denver | Chicago | Houston | Jacksonville | Los Angeles | Miami | New York City | Phoenix | Portland | Sacramento | Salt Lake City | San Diego | San Francisco | Seattle | Washington D.C. Canada Ottawa | Toronto | Vancouver Mexico Mexico City Benefits At Scribd, Inc. Scribd Flex (flexible work model) Comprehensive health, dental, and vision coverage Mental health support and disability coverage Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals Paid parental leave and family support benefits Retirement matching and employee equity Learning and development programs and professional growth opportunities Wellness and home office stipends Complimentary access to the Scribd, Inc. suite of products Enterprise access to leading AI tools Get to Know Scribd, Inc. About Scribd, Inc. Life at Scribd, Inc. We want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make to better accommodate your needs by emailing accommodations@scribd.com about the need for adjustments at any point in the interview process. If you apply for a job with Scribd, Inc. or otherwise engage with us in connection with employment (including as an employee, contractor, or other personnel), the personal information we process in that context is subject to our Employee and Applicant Privacy Policy, which is available here . Scribd, Inc. is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.

The Company PayPal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, PayPal empowers consumers and businesses in approximately 200 markets to join and thrive in the global economy. We operate a global, two-sided network at scale that connects hundreds of millions of merchants and consumers. We help merchants and consumers connect, transact, and complete payments, whether they are online or in person. PayPal is more than a connection to third-party payment networks. We provide proprietary payment solutions accepted by merchants that enable the completion of payments on our platform on behalf of our customers. We offer our customers the flexibility to use their accounts to purchase and receive payments for goods and services, as well as the ability to transfer and withdraw funds. We enable consumers to exchange funds more safely with merchants using a variety of funding sources, which may include a bank account, a PayPal or Venmo account balance, PayPal and Venmo branded credit products, a credit card, a debit card, certain cryptocurrencies, or other stored value products such as gift cards, and eligible credit card rewards. Our PayPal, Venmo, and Xoom products also make it safer and simpler for friends and family to transfer funds to each other. We offer merchants an end-to-end payments solution that provides authorization and settlement capabilities, as well as instant access to funds and payouts. We also help merchants connect with their customers, process exchanges and returns, and manage risk. We enable consumers to engage in cross-border shopping and merchants to extend their global reach while reducing the complexity and friction involved in enabling cross-border trade. Our beliefs are the foundation for how we conduct business every day. We live each day guided by our core values of Inclusion, Innovation, Collaboration, and Wellness. Together, our values ensure that we work together as one global team with our customers at the center of everything we do – and they push us to ensure we take care of ourselves, each other, and our communities. Job Summary: This job will design, develop, and implement machine learning models and algorithms to solve complex problems. You will work closely with data scientists, software engineers, and product teams to enhance services through innovative AI/ML solutions. Your role will involve building scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences. Job Description: Essential Responsibilities: Develop and optimize machine learning models for various applications. Preprocess and analyze large datasets to extract meaningful insights. Deploy ML solutions into production environments using appropriate tools and frameworks. Collaborate with cross-functional teams to integrate ML models into products and services. Monitor and evaluate the performance of deployed models. Minimum Qualifications: 3+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn. Familiarity with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment. Several years of experience in designing, implementing, and deploying machine learning models. Additional Responsibilities & Preferred Qualifications: Subsidiary: PayPal Travel Percent: 0 The base pay for this role will depend on where you work and the relevant experience and expertise you bring. The expected range of pay for this role by location is: Primary Location : Pay Range: San Jose, California: ($159,500.00 - $236,500.00 Annually) Additional Location(s) : Pay Range: No other locations are assigned to this requisition currently. Additional compensation for this role may include an annual performance bonus, equity, or other incentive compensation, as applicable. PayPal does not charge candidates any fees for courses, applications, resume reviews, interviews, background checks, or onboarding. When making an application directly, we will never ask you to share passwords, one-time passcodes (OTP), or verification codes. Any such request is a red flag and likely part of a scam. All communication regarding your application will come from official PayPal email domains. If you suspect fraudulent activity, please report it immediately. To learn more about how to identify and avoid recruitment fraud please visit https://careers.pypl.com/contact-us. For the majority of employees, PayPal's balanced hybrid work model offers 3 days in the office for effective in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace, ensuring that you equally have the benefits and conveniences of both locations. Our Benefits: At PayPal, we're committed to building an equitable and inclusive global economy. And we can't do this without our most important asset-you. That's why we offer comprehensive, choice-based programs, to support all aspects of personal wellbeing—physical, emotional, and financial—delivering meaningful value where it matters most. We strive to create a flexible, balanced work culture with a holistic approach to benefits, including generous paid time off, healthcare coverage for you and your family, and resources to create financial security and support your mental health. Who We Are: Click Here to learn more about our culture and community. Commitment to Diversity and Inclusion PayPal provides equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, pregnancy, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state, or local law. In addition, PayPal will provide reasonable accommodations for qualified individuals with disabilities. If you are unable to submit an application because of incompatible assistive technology or a disability, please contact us at paypalglobaltalentacquisition@paypal.com. Belonging at PayPal: Our employees are central to advancing our mission, and we strive to create an environment where everyone can do their best work with a sense of purpose and belonging. Belonging at PayPal means creating a workplace with a sense of acceptance and security where all employees feel included and valued. We are proud to have a diverse workforce reflective of the merchants, consumers, and communities that we serve, and we continue to take tangible actions to cultivate inclusivity and belonging at PayPal. Any general requests for consideration of your skills, please Join our Talent Community. We know the confidence gap and imposter syndrome can get in the way of meeting spectacular candidates. Please don't hesitate to apply.

Machine Learning Engineer Join an industry-leading life sciences technology organization that develops advanced software and AI solutions used by pharmaceutical, biotechnology, and research organizations worldwide. Their flagship AI platform brings cutting-edge machine learning directly into the workflows scientists use every day, helping drug discovery teams make faster, data-driven decisions. The platform combines large-scale scientific datasets, predictive modeling, and cloud-based infrastructure to accelerate the identification and development of new therapies. About The Role We are seeking a product-focused Machine Learning Engineer to help build and scale a next-generation AI platform used in drug discovery and scientific research. This individual will work across the full machine learning lifecycle, from data preparation and experimentation through model development, deployment, and optimization. You will collaborate closely with software engineers, scientists, and product teams to transform advanced machine learning research into reliable, production-ready solutions that deliver real-world impact. What You'll Do Design, build, and deploy machine learning models that support scientific discovery and predictive analytics. Develop solutions involving graph neural networks, transfer learning, molecular prediction models, and modern deep learning techniques. Create scalable training, evaluation, and model selection pipelines. Curate and improve large scientific datasets while ensuring data quality and reproducibility. Partner with engineering teams to deploy containerized ML services in cloud-based environments. Research and evaluate emerging machine learning approaches for incorporation into the platform. Qualifications 5+ years of experience building and delivering machine learning solutions in production environments. Strong Python development skills. Deep experience with PyTorch, TensorFlow, JAX, or similar frameworks. Strong understanding of model training, evaluation, optimization, and deployment. Experience translating complex machine learning problems into measurable business outcomes. Preferred Experience in life sciences, chemistry, drug discovery, bioinformatics, or related scientific domains. Knowledge of RDKit, Chemprop, federated learning, Kubernetes, Docker, and cloud-based ML infrastructure. This is an opportunity to work on highly impactful AI products that directly support scientific innovation and the development of life-changing therapies. Compensation: $180,000 - $250,000 base salary (Based on experience) plus benefits and equity If you are interested, please apply directly for immediate consideration or reach out to Brian Samela at bsamela@talener.com

About Canals Canals builds software for wholesale distributors, helping them operate more efficiently through automation and AI. Our customers are the companies responsible for moving the materials that power the real economy; electrical supplies, plumbing products, roofing materials, HVAC equipment, and more. Every day, thousands of people rely on Canals to help process orders, manage purchasing, handle accounts payable, and streamline critical business workflows. We're a profitable, rapidly growing company with a team of roughly 140 people distributed across North and South America. We care deeply about building great products, hiring exceptional people, and creating an environment where talented individuals can do the best work of their careers. The Role Our customer base is expanding fast, and AI is central to how we scale and deliver value. We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world impact. What You'll Do Design, build, and maintain scalable machine learning models that improve and automate logistics processes for our customers. Own projects end-to-end, from problem definition and data exploration to model deployment and monitoring in production. Collaborate closely with engineering teams to align ML work with customer needs and deliver features that drive business value. Serve as a technical leader and mentor within the ML area, reviewing code and ensuring best practices for reproducibility, quality, and performance. Evaluate and implement tools and frameworks to improve our ML infrastructure and workflows. Help shape the future of Canals as we continue scaling with our customers. What You'll Bring Senior-level experience building and deploying machine learning models in production environments. Experience designing scalable data pipelines and working with large datasets. Comfort taking ownership of projects and ensuring models deliver real, measurable customer value. Strong Python skills with knowledge of ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow) and data tools (e.g., Pandas, Spark). Ability to guide and unblock others, providing thoughtful code reviews and architectural feedback. Experience working independently in a fast-paced, product-focused environment. Previous experience in high-growth startups or small teams is a plus. Familiarity with MLOps practices and tools is a plus. Why Join Canals We're building software that solves real problems for an industry that keeps the world running. Our customers rely on our platform every day to operate their businesses. We've found strong product-market fit and continue to grow quickly, creating opportunities for people who want to have a meaningful impact on the trajectory of a company. We believe great people build great companies. That's why we invest heavily in hiring, development, and creating an environment where talented individuals can do the best work of their careers. You'll work alongside ambitious, thoughtful teammates who care deeply about what they do, challenge each other directly, and have a lot of fun along the way. We value ownership, transparency, and continuous improvement. Good ideas can come from anywhere, and people are trusted to make things happen. We're remote-first, flexible, and distributed across North and South America, bringing together talented people from a wide range of backgrounds and experiences. Canals.ai is an equal opportunity employer. In addition to EEO being the law, it is a policy that is fully consistent with our principles. All qualified applicants will receive consideration for employment without regard to status as a protected veteran or a qualified individual with a disability, or other protected status such as race, religion, color, national origin, sex, sexual orientation, gender identity, genetic information, pregnancy or age.

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. Responsibilities Design, develop, and train cutting-edge world class machine learning models (including NER, personalization, prediction, and LLM fine-tuning) to power Atlassian products. Ship production-ready ML systems by collaborating with engineering, product, and analytics teams to integrate AI features into Atlassian services as well as performing machine learning model optimization techniques and writing production quality code. Conduct experimentation and model evaluation, ensuring performance, scalability, and reliability of deployed models. Define and monitor product metrics, using data analysis to guide improvements and measure impact. Contribute to Atlassian's next-gen AI initiatives, including foundational model development and researching and advancing the state of ML techniques to land new unique features for Atlassian's AI offerings. Communicate findings and recommendations to senior leaders through clear presentations and insights. 40 hrs/week, Mon-Fri, 8:30 a.m. - 5:30 p.m. Salary: $276,200/yr. 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. Qualifications MINIMUM REQUIREMENTS: Master's degree or foreign equivalent degree in Computer Science, Electrical Engineering, or a related field of study, and three (3) years of related work experience. Must Have Three (3) Years Of Experience With/in Spark and Cloud data environments (AWS, Databricks, or Azure) Building and scaling machine learning models using large amounts of data Utilizing Python or Java to write performant production-quality code Anomaly detection for dataset analysis for the purpose of training, validating, and shipping machine learning models Agile development practices SQL BI tooling using Tableau, Excel, Power BI, or Looker 100% Telecommuting is permitted. Please email resume to: postings@atlassian.com and must reference Job code: "CA013" to be considered 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.

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. Sales Territory: $179,400 - $204,700 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 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).

About Canals Canals builds software for wholesale distributors, helping them operate more efficiently through automation and AI. Our customers are the companies responsible for moving the materials that power the real economy; electrical supplies, plumbing products, roofing materials, HVAC equipment, and more. Every day, thousands of people rely on Canals to help process orders, manage purchasing, handle accounts payable, and streamline critical business workflows. We're a profitable, rapidly growing company with a team of roughly 140 people distributed across North and South America. We care deeply about building great products, hiring exceptional people, and creating an environment where talented individuals can do the best work of their careers. The Role Our customer base is expanding fast, and AI is central to how we scale and deliver value. We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world impact. What You'll Do Design, build, and maintain scalable machine learning models that improve and automate logistics processes for our customers. Own projects end-to-end, from problem definition and data exploration to model deployment and monitoring in production. Collaborate closely with engineering teams to align ML work with customer needs and deliver features that drive business value. Serve as a technical leader and mentor within the ML area, reviewing code and ensuring best practices for reproducibility, quality, and performance. Evaluate and implement tools and frameworks to improve our ML infrastructure and workflows. Help shape the future of Canals as we continue scaling with our customers. What You'll Bring Senior-level experience building and deploying machine learning models in production environments. Experience designing scalable data pipelines and working with large datasets. Comfort taking ownership of projects and ensuring models deliver real, measurable customer value. Strong Python skills with knowledge of ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow) and data tools (e.g., Pandas, Spark). Ability to guide and unblock others, providing thoughtful code reviews and architectural feedback. Experience working independently in a fast-paced, product-focused environment. Previous experience in high-growth startups or small teams is a plus. Familiarity with MLOps practices and tools is a plus. Why Join Canals We're building software that solves real problems for an industry that keeps the world running. Our customers rely on our platform every day to operate their businesses. We've found strong product-market fit and continue to grow quickly, creating opportunities for people who want to have a meaningful impact on the trajectory of a company. We believe great people build great companies. That's why we invest heavily in hiring, development, and creating an environment where talented individuals can do the best work of their careers. You'll work alongside ambitious, thoughtful teammates who care deeply about what they do, challenge each other directly, and have a lot of fun along the way. We value ownership, transparency, and continuous improvement. Good ideas can come from anywhere, and people are trusted to make things happen. We're remote-first, flexible, and distributed across North and South America, bringing together talented people from a wide range of backgrounds and experiences. Canals.ai is an equal opportunity employer. In addition to EEO being the law, it is a policy that is fully consistent with our principles. All qualified applicants will receive consideration for employment without regard to status as a protected veteran or a qualified individual with a disability, or other protected status such as race, religion, color, national origin, sex, sexual orientation, gender identity, genetic information, pregnancy or age.

Archer is collaborating with athenahealth to connect them with exceptional professionals for this role. Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all. The Senior Machine Learning Engineer is responsible for designing, developing, deploying, and optimizing machine learning solutions that support healthcare products and analytics initiatives across athenahealth. Based in Boston, MA in a hybrid work model, this role partners with cross-functional teams to apply modern machine learning, data science, and software engineering practices to meaningful healthcare challenges. The individual in this role will contribute across the full machine learning lifecycle, from identifying opportunities and evaluating approaches to deploying production services and improving model performance over time. This position reports to the Data Science Manager. Team Summary The athenaClinicals product is a vital component of the athenaOne platform, enabling strong experiences for clients and users across clinical workflows. The Data Science team applies machine learning, advanced analytics, and modern engineering practices to automate existing workflows and create more efficient, intelligent solutions. In partnership with product and engineering leaders across the company, the team works to embed machine learning capabilities into athenahealth's suite of products in ways that improve usability, increase automation, and support innovation. This team focuses on applying machine learning to complex healthcare problems across a variety of products and domains. Team members work closely with platform engineers and cross-functional partners to develop, deploy, and scale state-of-the-art machine learning models using cloud technologies and production-grade engineering practices. Work is typically executed within scrum teams of 2–4 people, with close collaboration across technical and non-technical stakeholders to deliver practical, measurable outcomes. Essential Job Responsibilities Identify opportunities to apply machine learning techniques to healthcare product and business problems and evaluate which approaches are most appropriate. Design and develop machine learning models and ML-based production services for client-facing and internal applications. Build scalable data pipelines, feature engineering workflows, and training datasets using structured and unstructured data. Deploy and maintain production machine learning services using cloud infrastructure and machine learning operations practices. Apply rigorous testing and validation methods to statistics, models, code, and production workflows to support quality and reliability. Follow and contribute to conventions and best practices for modeling, coding, architecture, and statistical methods. Collaborate effectively with colleagues across technical and non-technical functions to define requirements, communicate findings, and deliver solutions. Contribute to the development of internal tools, reusable frameworks, and team standards that improve the effectiveness of data science work. Use artificial intelligence tools to improve experimentation, coding, analysis, and workflow efficiency, while reviewing outputs carefully and applying sound judgment to technical decisions. Monitor model and service performance and improve solutions over time based on operational insights, changing requirements, and business impact. Additional Job Responsibilities Support exploratory analyses, proofs of concept, and prototype development for emerging machine learning opportunities. Partner with platform and infrastructure teams to improve tooling for model training, deployment, observability, and reproducibility. Assist in establishing best practices for experiment tracking, model versioning, feature management, and continuous integration and continuous deployment. Prepare technical summaries, recommendations, and presentations for stakeholders across a range of technical backgrounds. Evaluate new tools, frameworks, and methodologies relevant to machine learning engineering, data science, and generative artificial intelligence. Participate in incident analysis and remediation efforts related to machine learning-enabled systems. Provide technical guidance and knowledge sharing to peers through collaboration, feedback, and documentation. Contribute to roadmap planning, estimation, and prioritization for machine learning and data science initiatives. Expected Education & Experience Bachelor's or Master's degree in Mathematics, Computer Science, Data Science, Statistics, or a related quantitative field, or equivalent practical experience. 4 to 6 years of professional hands-on experience developing, evaluating, and deploying machine learning models in production environments. Proficiency in Python, Structured Query Language (SQL), and Unix-based development environments. Experience building, testing, and maintaining production-grade machine learning services and workflows. Knowledge of machine learning fundamentals, statistical methods, model evaluation, and software engineering best practices. Familiarity with natural language processing, computer vision, or other applied machine learning techniques. Experience with deep learning models and complex neural network architectures is helpful. Experience training or fine-tuning large language models and generative artificial intelligence models is helpful. Experience with cloud platforms such as Amazon Web Services, including technologies such as Kubernetes, Kubeflow, or Elastic Kubernetes Service, is helpful. Strong communication skills, including the ability to communicate clearly in writing and in conversation with technical and non-technical audiences. Expected Compensation $145,000 - $247,000 The base salary range shown reflects the full range for this role from minimum to maximum. At athenahealth, base pay depends on multiple factors, including job-related experience, relevant knowledge and skills, how your qualifications compare to others in similar roles, and geographical market rates. Base pay is only one part of our competitive Total Rewards package - depending on role eligibility, we offer both short and long-term incentives by way of an annual discretionary bonus plan, variable compensation plan, and equity plans. About Athenahealth Our vision: In an industry that becomes more complex by the day, we stand for simplicity. We offer IT solutions and expert services that eliminate the daily hurdles preventing healthcare providers from focusing entirely on their patients — powered by our vision to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all. Our company culture: Our talented employees — or athenistas, as we call ourselves — spark the innovation and passion needed to accomplish our vision. We are a diverse group of dreamers and do-ers with unique knowledge, expertise, backgrounds, and perspectives. We unite as mission-driven problem-solvers with a deep desire to achieve our vision and make our time here count. Our award-winning culture is built around shared values of inclusiveness, accountability, and support. Our DEI commitment: Our vision of accessible, high-quality, and sustainable healthcare for all requires addressing the inequities that stand in the way. That's one reason we prioritize diversity, equity, and inclusion in every aspect of our business, from attracting and sustaining a diverse workforce to maintaining an inclusive environment for athenistas, our partners, customers and the communities where we work and serve. What We Can Do For You Along with health and financial benefits, athenistas enjoy perks specific to each location, including commuter support, employee assistance programs, tuition assistance, employee resource groups, and collaborative workspaces — some offices even welcome dogs. We also encourage a better work-life balance for athenistas with our flexibility. While we know in-office collaboration is critical to our vision, we recognize that not all work needs to be done within an office environment, full-time. With consistent communication and digital collaboration tools, athenahealth enables employees to find a balance that feels fulfilling and productive for each individual situation. In addition to our traditional benefits and perks, we sponsor events throughout the year, including book clubs, external speakers, and hackathons. We provide athenistas with a company culture based on learning, the support of an engaged team, and an inclusive environment where all employees are valued. Learn more about our culture and benefits here: athenahealth.com/careers https://www.athenahealth.com/careers/equal-opportunity