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Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. At Boeing, we innovate and collaborate to make the world better. Boeing Defense, Space & Security (BDS) is seeking a Full Stack Data Scientist to join the Business Transformation organization in support of modernizing how BDS operates through artificial intelligence, machine learning, and advanced digital capabilities. This role will help build and scale the technical foundations that enable secure, reliable, and production-ready AI/ML solutions across the enterprise. This position is ideal for a data scientist who brings a blend of data science, data engineering, MLOps, platform engineering, DevOps, and site reliability engineering (SRE) experience. The selected candidate will help design, implement, secure, and operate the infrastructure and platform services that support data pipelines, model training, model deployment, observability, and AI application delivery. The role will work across on-premises, hybrid, and cloud-based environments, with a strong emphasis on enterprise-grade security, resiliency, traceability, and operational excellence. The full stack data scientist in this role will partner closely with data scientists, software engineers, data engineers, cybersecurity teams, infrastructure teams, and business stakeholders to accelerate adoption of AI/ML capabilities within BDS. This includes enabling both traditional machine learning workflows and emerging generative AI use cases, including support for on-premises model serving, retrieval-augmented generation architectures, and modern model lifecycle management practices. The role requires strong systems thinking and the ability to translate business and mission needs into scalable platform solutions. The successful candidate will be expected to contribute both strategically and hands-on. They will help define best practices, build reusable patterns, automate infrastructure and delivery workflows, improve platform reliability, and support secure deployment of mission-relevant AI/ML systems. Depending on level, this role may also provide technical leadership, influence architecture decisions, and mentor other data scientists across the organization. This position offers the opportunity to shape the future of AI/ML enablement within Boeing BDS by building the secure and modern technical ecosystem needed to support transformation at scale. Key Responsibilities: Design, build, deploy, and operate secure, scalable AI/ML platform capabilities for Boeing BDS users and programs Develop and maintain data pipelines, workflow orchestration solutions, and data integration services that support analytics and machine learning use cases Build and support MLOps workflows for model training, experiment tracking, model registry, deployment, inference, monitoring, and lifecycle management Engineer and operate infrastructure for traditional machine learning and generative AI workloads, including on-premises and hybrid model serving environments Deploy, administer, and optimize containerized applications and platform services using Kubernetes and associated ecosystem tools Create and maintain Helm charts, deployment templates, automation scripts, and reusable platform patterns for AI/ML applications Implement CI/CD and GitOps workflows to support secure, repeatable, and observable software and model delivery processes Configure and support observability capabilities including metrics, logs, traces, dashboards, alerting, and reliability reporting Apply DevSecOps principles across application and infrastructure delivery, including certificate management, secrets handling, access control, least privilege, and defense-in-depth controls Support administration and integration of data storage technologies including relational, NoSQL, search, graph, object, and vector data platforms Build and maintain secure container images and software supply chain controls, including vulnerability scanning and artifact management Collaborate with data scientists and application teams to productionize models, improve runtime performance, and reduce friction in AI/ML development workflows Troubleshoot complex platform, data, networking, deployment, and performance issues across distributed environments Develop infrastructure-as-code and automation solutions to provision, configure, and manage platform resources Contribute to architecture decisions, platform standards, technical documentation, operational runbooks, and engineering best practices Support incident response, root cause analysis, reliability improvement efforts, and continuous service optimization To meet U.S. export control compliance requirements, a "U.S. Person" as defined by 22 C.F.R. 120.62 is required. "U.S. Person" includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee Basic Qualifications (Required Skills / Experience) : Bachelor's degree or higher in computer science, computer engineering, software engineering, data engineering, information technology, mathematics, or a related technical field 3+ years of experience in software engineering, platform engineering, DevOps, site reliability engineering, data engineering, MLOps, machine learning infrastructure, or a related technical discipline 3+ years of experience developing software and automation solutions using Python 3+ years of experience building, deploying, operating, and orchestrating containerized applications using tools such as Docker, Podman, Buildah, and Kubernetes 3+ years of experience designing or supporting CI/CD pipelines and modern software delivery practices 3+ years of experience working with data pipeline orchestration tools, building production data workflows, databases, or data platforms in production environments 3+ years of experience implementing monitoring, logging, alerting, or observability solutions for production systems 3+ years of experience working in Linux-based environments and troubleshooting distributed systems Preferred Qualifications (Desired Skills / Experience) : Master's in Computer Science, Machine Learning, Applied Mathematics, Computer Engineering, Software Engineering, Artificial Intelligence, Physics, or a closely related technical field Experience applying foundational cybersecurity practices in application or infrastructure engineering environments Experience designing and operating end-to-end AI/ML, data, and MLOps platforms, including data pipelines, experiment tracking, model registry, deployment, inference, monitoring, governance, and lifecycle management Experience with production data engineering and analytics platforms using workflow orchestration tools, Python data processing libraries, and relational, NoSQL, graph, search, vector, object, and analytical data storage technologies Experience with Kubernetes-based platform engineering, containerized application deployment, Helm, enterprise Kubernetes distributions, and GitOps/CI/CD workflows using tools such as ArgoCD and GitLab Experience with observability, reliability engineering, and infrastructure automation, including Prometheus, Grafana, Loki, Tempo, Mimir, OpenTelemetry, SLIs/SLOs, incident response, Terraform, OpenTofu, Pulumi, Ansible, shell scripting, and Python automation Experience with secure containerization, software supply chain security, and DevSecOps practices, including hardened container images, SBOM generation, image signing, vulnerability scanning, TLS, certificate management, secrets management, RBAC, least privilege, and policy enforcement Experience supporting machine learning and generative AI workloads in enterprise environments, including model serving, model monitoring, evaluation, on-premises or hybrid LLM deployment, and retrieval-augmented generation architectures Knowledge of AWS data and machine learning services and experience working in defense, aerospace, regulated, or other high-assurance environments requiring secure, reliable, and auditable technical solutions Typical Education / Experience: Education/experience typically acquired through advanced technical education (e.g. Bachelor) and typically 9 or more years' related work experience, or an equivalent combination of technical education and experience (e.g. PhD + 4 years' related work experience, Master + 7 years' related work experience, 13 years' related work experience, etc.). Typical Education/Experience: Education/experience typically acquired through advanced technical education (e.g. Bachelor) and typically 5 or more years' related work experience or an equivalent combination of technical education and experience (e.g. PhD, Master+3 years' related work experience, 6years' related work experience, etc.) Conflict of Interest: Successful candidates for this job must satisfy the Company's Conflict of Interest (COI) assessment process. Relocation: Relocation assistance is not a negotiable benefit for this position. Candidates must live in the immediate area or relocate at their own expense. Travel: Position may require travel up to 10% of the time. Drug Free Workplace: Boeing is a Drug Free Workplace where post offer applicants and employees are subject to testing for marijuana, cocaine, opioids, amphetamines, PCP, and alcohol when criteria is met as outlined in our policies. Pay & Benefits: At Boeing, we strive to deliver a Total Rewards package that will attract, engage and retain the top talent. Elements of the Total Rewards package include competitive base pay and variable compensation opportunities. The Boeing Company also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and a number of programs that provide for both paid and unpaid time away from work. The specific programs and options available to any given employee may vary depending on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements. Pay is based upon candidate experience and qualifications, as well as market and business considerations. Summary pay range: $137,700-$186,300 Applications for this position will be accepted until Oct. 23, 2026 Export Control Requirements: This position must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a "U.S. Person" as defined by 22 C.F.R. 120.62 is required. "U.S. Person" includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee. Export Control Details: US based job, US Person required Education Bachelor's Degree or Equivalent Required Relocation Relocation assistance is not a negotiable benefit for this position. Visa Sponsorship Employer will not sponsor applicants for employment visa status. Shift This position is for 1st shift Equal Opportunity Employer: Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.

About Gallatin At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage. What You'll Do In this role, you will build the systems that move a model out of a notebook and into the hands of a planner. Sometimes, that means deploying to an air-gapped rack in a tent instead of a VPC. You will own the path from training run to deployed capability: the infrastructure it runs on, the release process that ships it, the evaluation harness that proves it works, and the telemetry that tells us when it stops working. Our AI/ML team works across retrieval-grounded systems for doctrine and logistics data, document and feature extraction, military symbol recognition, optimization and movement models, and an LLM agent platform. This role underpins that work: building the infrastructure, release processes, and evaluation systems that make it shippable and keep it honest in production. You will have plenty of room to shape how we build it. Training & Serving Infrastructure Own model training, fine-tuning, and batch inference infrastructure across AWS (SageMaker, EKS) and on-premises GPU hardware. Stand up and tune LLM inference serving: vLLM-class stacks, quantization, continuous batching, KV-cache and throughput sizing. Make the hosted-vs-local call with numbers behind it. Build for DDIL: local inference with configurable fallback, degraded-mode behavior, and sane resource envelopes on hardware we do not get to choose. Release & Reproducibility Build CI/CD for models and pipelines: versioned datasets, a model registry, promotion gates, and rollback that actually works under pressure. Own infrastructure as code, containerization, and GitOps deployment across environments ranging from a dev cluster to a disconnected enclave. Make reproducibility a hard requirement. Any result we put in front of a customer or evaluator must be reproducible from a commit and dataset version. Evaluation & Observability Build and own the evaluation harness: regression suites for retrieval and extraction pipelines, LLM-as-judge pipelines with measured judge-human agreement, and adversarial and held-out sets. Instrument production for drift, latency, cost, retrieval quality, and failure modes, including quiet ones such as a retrieval miss that produces a fluent but wrong answer. Make our metrics defensible to external test and evaluation reviewers. "We think it's good" is not a deliverable. Data & Pipeline Ownership Own ingestion, versioning, and lineage for logistics and doctrinal data drawn from a heterogeneous set of authoritative sources. Build and operate embedding and feature pipelines, incremental indexing, and the unglamorous systems that keep a retrieval index fresh. Build the human-in-the-loop infrastructure: confidence-scored routing, review queues, and feedback capture that improves the next model. Secure & Accredited Deployment Deploy and operate ML systems in IL5 and IL6 environments, including air-gapped or restricted-network enclaves. Build the release, observability, artifact-management, and incident-response workflows those environments require. Support ATO and continuous-authorization work with implementation evidence tied to applicable security controls (NIST SP 800-171, NIST SP 800-53 Rev. 5, CMMC Level 2, FIPS 140-3, and RMF/eMASS). Handle CUI and classified data correctly without being asked twice. What We're Looking For Strong Platform & Infrastructure Skills 5+ years in MLOps, ML platform, or infrastructure engineering, including meaningful time working on systems with real users. Strong Python skills and comfort in a production codebase, not just notebooks. Deep Kubernetes and containerization experience, plus infrastructure as code. Production experience with AWS ML/Azure infrastructure (SageMaker, EKS, or equivalent). Hands-on GPU infrastructure experience: scheduling, utilization, memory sizing, and cost. Hands-on experience deploying and operating production software in IL5 or IL6 environments, including disconnected or restricted-network deployments. Production ML Judgment You have shipped an LLM or ML system to production and then had to keep it working. You know what breaks. You have built evaluation and monitoring for ML systems rather than adopting a vendor dashboard and hoping. You can reason about where a pipeline's quality actually comes from and say so when a metric is measuring the wrong thing. Ownership You are comfortable with ambiguity and owning a domain end to end. This is a small team; there is no one to hand the pager to. You are willing to learn the mission domain. The engineers who do best here become genuinely interested in the logistics problem itself. Nice to Have Clearance: Preferred LLM serving and inference optimization (vLLM, TensorRT-LLM, quantization, prefix caching). Retrieval-grounded systems in production: hybrid retrieval, re-ranking, index freshness, and citation quality. Edge, on-premises, or disconnected deployment. Experience supporting ATO, continuous authorization, or production operations in classified environments. Defense, intelligence, or another accredited or regulated environment. Palantir Foundry, PostgreSQL/pgvector, NATS/JetStream, or ArgoCD. A degree in CS, engineering, or a related technical field, or the equivalent built the hard way. Mission and Identity We are building the system that enables faster, smarter logistics decisions in contested environments, and we're doing it with a team of seasoned entrepreneurs, operators, and technologists who have built and scaled solutions in this space before. We hold ourselves to an extremely high standard. We value clear thinking, direct communication, and the kind of ownership that doesn't stop until something actually works. Our mission is to create decision advantage when the stakes are the highest. If we succeed, the system doesn't just run; it gets smarter. We're not building AI for its own sake. We're building it because faster, smarter decisions in the most demanding environments on earth can't wait. If you want to work somewhere the stakes are real and the mission is urgent — you'll fit in here. Why Gallatin? The logistics infrastructure that supports America's warfighters and humanitarian disaster responders is overdue for transformation, and we are building it. From defense operations to disaster response, we're solving the hardest problems that keep missions moving when it matters most. Join a team where the mission is the point. Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location. In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment. Gallatin is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable federal, state, or local law. This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary. We comply with the United States Department of Labor's Pay Transparency provision . Note: Due to the nature of certain government contracts held by this organization, U.S. citizenship is a requirement for all positions at Gallatin. Proof of citizenship will be required prior to employment if selected. Compensation Range: $80K - $210K

About The Role The role owns the infrastructure and pipelines that turn ML experiments into reliable production systems — CI/CD for models, scalable training infrastructure, feature stores, and low-latency serving environments. Working at the intersection of data science and platform engineering, this position sits on a team where deployment reliability, model reproducibility, and infrastructure cost efficiency are all first-class concerns, not afterthoughts. Key Responsibilities Design and maintain CI/CD pipelines for ML workflows using tools like GitHub Actions, GitLab CI, or Jenkins, with automated testing and validation gates for models and data Build and operate model serving infrastructure (KServe, Seldon, SageMaker endpoints, or Triton) supporting low-latency inference at production scale Implement experiment tracking and model registry workflows with MLflow, Weights & Biases, or SageMaker Experiments to guarantee reproducibility across environments Build monitoring and observability for production ML: data drift detection, latency/throughput dashboards, and automated retraining triggers using tools like Evidently, Prometheus, and Grafana Automate data and feature pipelines in partnership with data engineering, leveraging Airflow, Kubeflow, or Dagster orchestration Manage Kubernetes-based ML workloads, optimizing GPU utilization, autoscaling policies, and cloud spend across training and inference clusters Establish and document MLOps standards — model versioning, rollback procedures, and incident response — and champion them across data science teams What We Are Looking For 3–6 years of experience in MLOps, DevOps, or ML engineering, with at least 2 years deploying and operating ML systems in production Strong Python and Bash skills; proficiency with Docker and Kubernetes for containerizing and orchestrating ML workloads Hands-on experience with at least one cloud platform (AWS, GCP, or Azure) and its ML-specific services Practical experience with ML workflow tooling: MLflow, Kubeflow, Airflow, or equivalent pipelines and registries Solid grasp of production ML concerns: model versioning, canary deployments, drift monitoring, and retraining strategies Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience Bonus: Experience with GPU cost optimization, distributed training (Ray, Dask), infrastructure-as-code (Terraform), or serving LLMs at scale

AI Prompt Designer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: AI Prompt Designer Location: 1 00% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $100,000 Annually Experience Required: 6+ years Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. Job Summary We are looking for a AI Prompt Designer to define and lead the strategy, patterns, and tooling for designing prompts, agentic workflows, and LLM-based application architectures across the organization. The role combines deep practical mastery of modern LLMs with the discipline of building reusable design patterns, evaluation frameworks, and developer tooling that scale across many teams and use cases. The ideal candidate has shipped LLM-powered products in production, is fluent in prompt design patterns and agent architectures, and brings strong judgment about when and how to apply different LLM techniques responsibly. Required Qualifications Bachelor's or Master's degree in Computer Science, Computational Linguistics, or a related field. Six or more years of software engineering experience, with significant time on LLM-based applications. Demonstrated experience shipping LLM-powered products to production. Deep familiarity with modern LLM APIs and agent frameworks. Strong understanding of retrieval-augmented generation, embeddings, and vector databases. Experience designing evaluation pipelines for non-deterministic systems. Strong Python skills and comfort with modern application frameworks. Solid grasp of responsible AI principles, including safety and policy considerations. Excellent written and verbal communication skills. Track record of mentoring engineers and influencing technical direction. Preferred Qualifications Public writing, talks, or open-source contributions on LLM application development. Experience with multi-agent architectures and complex tool-use systems. Familiarity with fine-tuning workflows and when to choose them over prompting. Exposure to product domains such as customer support, coding assistants, or analytics agents. Experience integrating LLMs into enterprise software systems with strict compliance requirements. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to hilda@bvteck.com Bright Vision Technologies is an Equal Opportunity Employer. Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Title: GenAI Engineer Job Summary We are looking for a GenAI Engineer to design, develop, and integrate Generative AI solutions into applications and business processes. The ideal candidate should have a basic understanding of Python, Large Language Models (LLMs), prompt engineering, RAG, APIs, and machine learning concepts . Key Responsibilities: Develop applications using Generative AI and Large Language Models (LLMs) . Build and test prompt engineering techniques for different use cases. Develop RAG (Retrieval-Augmented Generation) applications using company documents and knowledge bases. Integrate AI models through APIs such as OpenAI or other LLM providers. Work with vector databases for storing and searching document embeddings. Develop Python-based AI applications and automation workflows. Evaluate LLM responses for accuracy, relevance, and quality. Perform data preprocessing, document processing, and text extraction. Work with AI frameworks and libraries such as LangChain, LlamaIndex, or similar tools . Collaborate with software engineers, data scientists, and product teams to deploy AI solutions. Stay updated with new developments in Generative AI, LLMs, and AI tools . Required Skills Python programming Basic Machine Learning / Deep Learning knowledge Understanding of Generative AI and LLMs Prompt Engineering Understanding of RAG Experience with REST APIs Basic knowledge of embeddings and vector databases Familiarity with tools such as: OpenAI / Azure OpenAI LangChain LlamaIndex FAISS, Chroma, Pinecone, or similar vector databases Basic knowledge of Git and GitHub Good problem-solving and communication skills Good to Have Knowledge of AWS, Azure, or Google Cloud Experience with Docker Knowledge of SQL and databases Familiarity with AI agents and tool calling Basic understanding of model evaluation and AI safety Experience deploying Python applications using FastAPI or similar frameworks Education & Experience Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field. 5+ years of experience in software development, AI/ML, or Generative AI.

We are At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron's progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 17,000+, and has 58 offices in 22 countries within key global markets. Our challenge We are seeking a skilled GenAI Engineer with strong experience in Python, LangChain, and LangGraph to design, develop, and deploy intelligent AI applications and agentic workflows. The ideal candidate will have hands-on experience building production-ready solutions using large language models, retrieval-augmented generation, tool-calling, and multi-agent systems. Additional Information* The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within Charlotte, NC is $90k - $105k/year & benefits (see below). The Role Responsibilities: Design, develop, and deploy generative AI applications using large language models. Build LLM-powered workflows, assistants, chatbots, and automation solutions using LangChain and LangGraph . Develop agentic systems with capabilities such as:Tool and function calling Planning and reasoning Memory management Workflow orchestration Human-in-the-loop approvals Multi-agent collaboration Implement retrieval-augmented generation (RAG) pipelines using document processing, embeddings, vector databases, and semantic search. Integrate LLM applications with APIs, enterprise systems, databases, and third-party services. Develop robust prompts, structured outputs, evaluation frameworks, and guardrails. Optimize applications for accuracy, latency, scalability, cost, and reliability. Build and maintain reusable Python services, libraries, and APIs. Conduct testing and evaluation of LLM outputs, including relevance, factuality, safety, and consistency. Monitor production AI systems and troubleshoot performance or quality issues. Requirements: Strong professional experience developing applications using Python . Hands-on experience with LangChain and LangGraph . Solid understanding of:Large language models and transformer-based architectures Prompt engineering Embeddings and vector search Retrieval-augmented generation AI agents and workflow orchestration Function calling and structured data extraction Experience working with LLM providers such as OpenAI, Anthropic, Google Vertex AI/Gemini, or Azure OpenAI. Experience with REST APIs, microservices, and backend application development. Familiarity with vector databases such as Pinecone, Weaviate, Milvus, Chroma, or pgvector. Experience with Angular. We offer: A highly competitive compensation and benefits package. A multinational organization with 58 offices in 22 countries and the possibility to work abroad. 10 days of paid annual leave (plus sick leave and national holidays). Maternity & paternity leave plans. A comprehensive insurance plan including medical, dental, vision, life insurance, and long-/short-term disability (plans vary by region). Retirement savings plans. A higher education certification policy. Commuter benefits (varies by region). Extensive training opportunities, focused on skills, substantive knowledge, and personal development. On-demand Udemy for Business for all Synechron employees with free access to more than 5000 curated courses. Coaching opportunities with experienced colleagues from our Financial Innovation Labs (FinLabs) and Center of Excellences (CoE) groups. Cutting edge projects at the world's leading tier-one banks, financial institutions and insurance firms. A flat and approachable organization. A truly diverse, fun-loving, and global work culture. S YNECHRON'S DIVERSITY & INCLUSION STATEMENT Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative 'Same Difference' is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more. All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant's gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.

Responsibilities Team Introduction: We build the next-generation unified Agent system for TikTok's global e-Commerce customer service — running in 30+ languages across one of the largest e-Commerce surfaces on the internet. Our north star is a self-evolving Agent: post-training, harness, memory / context engineering, tools, and evaluation form one closed loop, and every served conversation becomes the next iteration's training / eval / retrieval / skill-induction signal. This loop is already running in production — cases are mined, root-caused, turned into constrained candidates, replayed against frozen regression sets, and shipped behind guardrails. Two things make this team different from most "LLM application" work: We build the agent runtime itself — Codex / Claude-Code-class — not prompts on top of a vendor API. Evaluation and experimentation are first-class systems, not an afterthought. A self-improving loop optimizes whatever signal you give it, so the hardest and most valuable engineering here is making the judgment trustworthy — not just making the model change. By combining generative recommendation, large recommendation models, multimodal representation learning, and cross-domain value modeling, the team works on some of the most important algorithmic problems in live commerce. Our goal is to improve user experience, optimize ecosystem efficiency, and drive sustainable business growth for TikTok Shop across global markets. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities: Agent runtime (harness / agent loop). Orchestrate skills, tools, and context; implement loop control & intervention, progressive disclosure, and behavior-level guardrails. Build the production safety layer — pre-flight budgets and timeout truncation, serve-time gates, shadow / swap-in answer delivery, and safe fallback paths. Context & memory for long multi-turn agents. Agentic memory (structured note-taking), context compaction / summarization, context editing / observation masking, and just-in-time (retrieve-then-load) retrieval. Treat context as an evolving, itemized playbook — with structured diffs and a deterministic curator — rather than an ever-growing prompt. Post-training & the data flywheel. SFT / DPO / RL to internalize rules into weights (so the prompt gets shorter, not longer), plus distillation to smaller serving models. Turn served conversations into training / eval / retrieval signals. Tools, Skills, and MCP. Tools-as-APIs, connectors, skill / tool search for large inventories, and skill-library governance — description conflicts, trigger evals, cross-skill mis-fire matrices, and on-demand loading instead of dumping every definition into context. Evaluation you can bet a launch on. LLM-as-judge with human-agreement calibration; statistical rigor — paired comparison, confidence intervals, repeated sampling, pass^k; held-out and time-rolling eval splits with overfitting alarms; cascaded scoring and cross-family judge panels to make evaluation affordable at scale. The self-evolving loop. Case mining → automatic root-cause → constrained candidate generation → replay verification against frozen regression sets → canary → flywheel. Build the plumbing that makes it auditable: candidate registry with exact runtime read-back, change lineage, and an archive of rejected candidates you can sample from next round. Online experimentation & causal readout. Shadow / canary / A-B, non-inferiority gates, traffic-split health, metric definitions that survive scrutiny, and off-policy counterfactual evaluation where live A/B isn't possible. Qualifications Minimum Qualification(s): Individuals who are completing or have recently completed a Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline Strong Python plus one of C++ / Go / Rust / Java Solid ML / DL / NLP fundamentals, with genuine hands-on experience with LLMs or agents (coursework, research, internship, competition, open-source, or a serious side project) Basic statistical literacy — you can compute a confidence interval, explain what a p-value does and doesn't mean, and tell the difference between "the number went up" and "the system got better" Able to read a paper or an engineering blog and turn it into working code Preferred Qualification(s) Have built the runtime, not just called an API — even at research / hobby / competition scale: your own agent loop / harness, a memory / context-management system, a RAG or tool-use agent, or a fine-tuned / post-trained model Hands-on experience in any one of the following — depth in one is enough, breadth welcome: Post-training: SFT / DPO / RLHF / RLAIF / RLVR, reward modeling, reward hacking and how to defend against it Agent systems: harness, context engineering, MCP / Skills, sub-agents, tool search Evaluation & experimentation: LLM-as-judge and judge calibration, pass^k, regression suites, A/B and non-inferiority testing, off-policy evaluation Self-improving / evolutionary systems: evolutionary program search, candidate archives and parent sampling, automatic prompt / context optimization, multi-objective (Pareto) selection and credit assignment Inference & serving: vLLM / TensorRT-LLM, MoE, KV / prompt caching, and the cost engineering that comes with it Multilingual NLP Publications (for PhD), strong competition results (ACM-ICPC / Kaggle / ML competitions), or notable open-source contributions Taste for the honest number. We'd rather hire someone who says "this result isn't significant yet" than someone who ships a green dashboard. If you've ever killed your own experiment because the evidence didn't hold up, tell us about it. e-Commerce or multilingual experience is a plus, not required Job Information 【For Pay Transparency】Compensation Description (Annually) The base salary range for this position in the selected city is $121600 - $243200 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. About TikTok TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo. 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. TikTok Accommodation TikTok 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/RA-request

Company Qualcomm Technologies, Inc. Job Area Engineering Group, Engineering Group > Modem Technologies Software General Summary This position is not eligible for Qualcomm immigration sponsorship.*** We are seeking an experienced Machine Learning Engineer specializing in Agentic AI to join our Core Enterprise AI team. The ideal candidate will design, develop, and deploy next-generation AI systems powered by Large Language Models (LLMs), Agentic AI, Hybrid AI architectures, Retrieval-Augmented Generation (RAG), and autonomous intelligent agents . This role focuses on building enterprise-grade AI solutions capable of reasoning, planning, tool usage, workflow orchestration, and autonomous task execution. Key Responsibilities Design and implement Agentic AI systems capable of autonomous reasoning, planning, tool usage, and multi-step execution to solve complex enterprise problems. Build and optimize RAG, Hybrid AI, and knowledge-grounded architectures that augment LLMs with enterprise data, external tools, and domain-specific context. Develop and advance self-improving AI agents leveraging feedback loops, reflection, reinforcement learning techniques, automated evaluations, memory systems, and continuous learning frameworks. Research, develop, and apply LLM post-training techniques, including supervised fine-tuning (SFT), preference optimization, reinforcement learning, distillation, and domain adaptation to improve model performance for enterprise use cases. Create robust evaluation, benchmarking, observability, and safety frameworks for measuring agent quality, reliability, efficiency, and business impact. Build and maintain multi-agent systems and orchestration frameworks that enable collaborative agent workflows and intelligent task automation. Collaborate with cross-functional teams to identify high-value opportunities and deliver scalable AI solutions that drive measurable business outcomes. Stay at the forefront of advancements in Agentic AI, reasoning models, post-training, model routing, autonomous agents, and enterprise AI architectures, bringing innovative solutions into production. Minimum Qualifications Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field. Preferred Qualifications Experience with popular LLM frameworks (Langchain, LlamaIndex, Transformers) Knowledge of prompt engineering and chain-of-thought techniques Experience with containerization and microservices architecture Background in Reinforcement Learning Contributions to open-source AI projects Experience with ML ops and model deployment pipelines Skills And Competencies Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow) Practical experience with LLM deployments and fine-tuning Experience with vector databases and embedding models Familiarity with modern AI/ML infrastructure and cloud platforms (AWS, GCP, Azure) Strong understanding of RAG architectures and implementation Strong problem-solving and analytical skills Excellent communication and collaboration abilities Experience with agile development methodologies Ability to balance multiple projects and priorities Strong focus on code quality and best practices Understanding of AI ethics and responsible AI development Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries). To all Staffing and Recruiting Agencies : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications. EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification. Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law. Pay Range And Other Compensation & Benefits $114,500.00 - $171,700.00 The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link. If you would like more information about this role, please contact Qualcomm Careers.

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title Applied AI Engineer Location: 100% Remote (Continental United States) Position Type: Full-time, Direct W2 Salary Range: $130,000–$180,000 Annually (based on experience) Experience Required: 10+ Years Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. Job Summary Bright Vision Technologies is seeking a highly experienced Applied AI Engineer with 10+ years of experience in Artificial Intelligence, Machine Learning, and Deep Learning to design, develop, and deploy enterprise-scale AI solutions. The ideal candidate will possess deep expertise in Python, PyTorch or JAX, Large Language Models (LLMs), Natural Language Processing (NLP), Computer Vision, and MLOps , with a proven track record of taking AI models from research to production. This role requires strong technical leadership, hands-on development, and the ability to architect scalable, secure, and high-performance AI systems. Key Responsibilities Design, develop, and deploy production-grade AI and machine learning solutions that address complex business challenges. Architect scalable training, inference, and MLOps pipelines for enterprise AI applications. Develop, fine-tune, evaluate, and optimize Large Language Models (LLMs) , deep learning models, and multimodal AI systems. Build AI applications leveraging Retrieval-Augmented Generation (RAG) , vector databases, embeddings, prompt engineering, and agentic AI frameworks. Design and implement machine learning pipelines for NLP, Computer Vision, recommendation systems, and predictive analytics. Optimize model performance, latency, scalability, and infrastructure utilization across cloud and distributed computing environments. Collaborate with data scientists, software engineers, product managers, and business stakeholders to deliver production-ready AI solutions. Establish best practices for model evaluation, Responsible AI, governance, security, monitoring, and lifecycle management. Mentor junior AI engineers and contribute to technical leadership, architecture reviews, and engineering best practices. Evaluate emerging AI technologies, foundation models, and frameworks to drive continuous innovation. Required Qualifications Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical discipline. 10+ years of professional experience in Artificial Intelligence, Machine Learning, Deep Learning, or Applied AI. Expert-level programming skills in Python with extensive experience using PyTorch, JAX, or TensorFlow . Proven experience developing and deploying Large Language Models (LLMs) , NLP, Computer Vision, and deep learning applications in production. Strong knowledge of distributed training, GPU optimization, model serving, and production ML deployment. Hands-on experience with MLOps , CI/CD pipelines, model monitoring, feature engineering, and automated ML workflows. Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP) for AI workloads. Strong understanding of machine learning algorithms, statistics, model evaluation, experimentation, and optimization techniques. Excellent problem-solving, communication, collaboration, and technical leadership skills. Preferred Qualifications Experience with LLM fine-tuning , Retrieval-Augmented Generation (RAG) , agentic AI , multimodal AI , and vector databases such as Pinecone, Weaviate, Milvus, or FAISS . Hands-on experience with AI orchestration frameworks such as LangChain , LlamaIndex , LangGraph , or similar technologies. Experience deploying AI applications using Kubernetes, Docker, Ray, or distributed inference frameworks. Publications in AI/ML conferences, open-source contributions, patents, or recognized technical leadership in the AI community. Knowledge of Responsible AI, AI governance, model security, fairness, explainability, and regulatory compliance. Ph.D. or Master's degree with a specialization in Artificial Intelligence, Machine Learning, Computer Vision, or Natural Language Processing. Interested in this opportunity? Apply today for immediate consideration! Email your updated resume: jaya@bvteck.com Call or Text: (908) 505-3545 Learn More Bright Vision Technologies is an Equal Opportunity Employer. Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Senior Machine Learning Engineer Remote | Direct Hire $160K – $175K Base We are seeking a hands-on Senior Machine Learning Engineer to architect, build, and own end-to-end production ML systems for a growing enterprise SaaS platform. This role focuses on developing intelligent systems that classify, normalize, and enrich large volumes of complex business and transactional data using machine learning, NLP, and modern AI technologies. Responsibilities Architect and develop end-to-end production ML systems. Build classification, categorization, entity-matching, and data-normalization models. Develop scalable ML pipelines for training, inference, and deployment. Implement confidence scoring, model monitoring, drift detection, and retraining. Leverage Python, PyTorch, NLP, embeddings, and LLMs where appropriate. Build reliable MLOps infrastructure supporting multiple enterprise customers. Own technical decisions and collaborate with engineering, data science, and product teams. Requirements 6+ years of ML Engineering, Applied AI, or related experience. Strong Python and experience with PyTorch, TensorFlow, or scikit-learn. Proven end-to-end ownership of production ML systems. Experience with classification, NLP, entity matching, or similar modeling challenges. Strong MLOps experience, including deployment, monitoring, and retraining. Experience with AWS, SageMaker, Docker, Kubernetes, or comparable cloud infrastructure. Excellent problem-solving, architecture, and communication skills. Ideal Candidate: An engineer who can independently design, build, deploy, monitor, and improve scalable machine learning systems - not simply train models or maintain existing infrastructure.

Sony Pictures Entertainment is seeking a talented data engineer to join the Insights, Strategy and Analytics (ISA) organization. The ISA team focuses on enhancing analytical capabilities and infrastructure for internal stakeholders across television production, distribution, and marketing. As the world's largest independent studio, Sony Pictures Television produces original content for streaming and linear networks globally, including popular series across various genres. Our series – from dramas like PLURIBUS, THE NIGHT AGENT, THE LAST OF US, and THE BOYS, to comedies like TWISTED METAL and COBRA KAI, to unscripted series like JEOPARDY!, WHEEL OF FORTUNE, and AMERICAN IDOL – are beloved by our fans and customers worldwide. The Data Engineer II designs and builds reliable, scalable ELT pipelines that power analytics, reporting, and AI/ML applications across Sony Pictures Entertainment. Ideal candidates have prior experience supporting data platforms, analytics, or business intelligence initiatives within a film or television studio, streaming platform, broadcast network, or similarly scaled media company. This role contributes to fast-paced, business-facing analytics initiatives, delivering data products and analytical capabilities that support strategic decision-making across the studio. Key Responsibilities Analytics Engineering: Translate complex business requests into robust data models, business metrics, and semantic layers using dbt and SQL. Apply analytics engineering best practices to ensure maintainability, reliability, and performance. Data Engineering: Support the ingestion of new sources into SPE's Snowflake data platform, working closely with central IT and cloud operations teams to define ingestion requirements and validate that pipelines meet business needs and adhere to SPE's data governance and security policies. Cross-Functional Product Delivery: Partner closely with product managers, strategic analysts, data scientists, data visualization teams, and business stakeholders to deliver impactful tools. Collaborate across disciplines to understand business requirements, harmonize data from disparate systems, and create ready-to-use datasets that enable reporting, advanced analytics, and decision-making. "One Team" Mindset: Contribute to a high-performance team, collaborating openly with peers to create a team culture of ownership, accountability, and continuous improvement. Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Business Analytics, or related fields; advanced degrees are a plus. 5+ years in analytics engineering, data engineering, or a related technical role in a production environment. Experience working with data supporting television, film, streaming, advertising-supported video (AVOD), FAST channels, content distribution, audience analytics, or media strategy. Familiarity with business entities, metrics, and datasets common to a television production and distribution studio, including content and title metadata, audience measurement, ratings, and content performance data, talent, production, licensing, and distribution datasets. Experience working with industry data providers such as IMDb, Gracenote, Luminate, Nielsen, or similar sources is highly desirable. Advanced SQL and Python skills. Experience building transformation pipelines in dbt is strongly preferred. Proficiency with modern cloud data platforms (e.g., Snowflake, GCP/BigQuery, AWS/Redshift) and with pipeline orchestration tools. Familiarity with CI/CD workflows, version control, and automated testing frameworks to meet data quality standards. Exposure to generative AI technologies and practical interest in applying AI-driven approaches to data engineering, data quality, metadata management, and analytics workflows. Strong problem-solving skills, attention to detail, and the ability to communicate effectively with both technical and non-technical stakeholders. The anticipated base salary for this position is $1250K to $160K. This role may also qualify for annual incentive and/or comprehensive benefits. The actual base salary offered will depend on a variety of factors, including without limitation, the qualifications of the individual applicant for the position, years of relevant experience, level of education attained, certifications or other professional licenses held, and if applicable, the location of the position. Sony Pictures Entertainment is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, national origin, disability, veteran status, age, sexual orientation, gender identity, or other protected characteristics. SPE will consider qualified applicants with arrest or conviction records in accordance with applicable law. To request an accommodation for purposes of participating in the hiring process, you may contact us at SPE_Accommodation_Assistance@spe.sony.com.

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI's technology actively supports operations worldwide. For more information, visit www.shield.ai . Follow Shield AI on LinkedIn , X , Instagram , and YouTube . Job Description: Shield AI is seeking a Senior Analytics Engineer to lead the design and delivery of trusted data products for Corporate FP&A, Accounting, Program Finance, GTM, and Strategic Finance. You will independently shape analytical solutions, establish shared standards, and turn business priorities into AI-enabled decision support on our Databricks platform. This senior individual contributor role combines hands-on engineering with cross-functional influence and mentorship to expand Finance's impact. What you'll do: Shape the analytics roadmap with Finance and GTM leaders, prioritizing high-impact products and resolving competing requirements. Build governed datasets and semantic models that support reporting, planning, scenario analysis, and strategic decisions. Set modeling and metric standards across domains, exercising judgment on architecture, quality, and maintainability. Develop conversational analytics and AI-enabled workflows, including Databricks Genie, grounded in trusted business context. Use AI coding assistants to accelerate delivery and explore new capabilities while maintaining rigorous validation and quality. Own business-facing reconciliation and acceptance, partnering with data engineering on reliable delivery. Mentor engineers and Finance partners, strengthening technical practices and self-service capabilities. Required qualifications: Substantial experience in analytics engineering, BI engineering, or financial data modeling, with advanced SQL and a record of delivering production data products. Demonstrated ability to lead ambiguous, cross-functional work from problem definition through adoption. Proficiency with AI coding assistants and sound judgment when reviewing generated work. Practical experience with AI-assisted analytics, ideally Databricks Genie; adjacent Snowflake Cortex or comparable experience is welcome. Sound architectural judgment, disciplined development practices, and experience mentoring others and influencing senior stakeholders. Preferred qualifications: Experience with Databricks, Python, semantic layers, or AI application development. Experience supporting FP&A, GTM, Strategic Finance, Accounting, or Program Finance. Familiarity with enterprise planning, financial controls, or high-growth business environments. $120,000 - $190,000 a year #LC Full-time regular employee offer package: Pay within range listed + Bonus + Benefits + Equity Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment) Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information. Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.