Top Machine Learning Jobs in San Francisco Bay Area, CA

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead hybrid and multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and research-to-production processes.

Build and evaluate applied machine learning and Generative AI solutions for product and business problems. Interns will contribute to models, agents, retrieval, experimentation, evaluation, and prototyping with mentorship from experienced engineers. Responsibilities include translating business needs into technical projects, analyzing errors and user feedback, measuring outcomes, and clearly documenting findings, trade-offs, limitations, and next steps.

Leads experimentation, causal inference, advanced statistical analysis, machine learning, and scalable data-product development. Partners with product, engineering, and research teams to generate insights, identify opportunities, influence executive decisions, and shape product strategy. Owns cross-functional projects, analytics roadmaps, reporting, stakeholder adoption, and methodological standards while translating complex data into actionable business outcomes.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.

Lead the design and delivery of scalable machine learning systems and customer-facing generative AI applications for Rovo Chat. Build agent harnesses that improve quality, reliability, and latency; advance state-of-the-art AI capabilities; lead engineers through technical design and launch; and collaborate cross-functionally with teams and internal customers. The role also involves applying a deep understanding of the machine learning project lifecycle and supporting platform-based product development.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Leads New York Life’s AI Center of Excellence across frontier AI provider relationships, AI software lifecycle standards, platform architecture, governance integration, talent acquisition, upskilling, and production delivery. Builds and manages West Coast AI engineering teams, evaluates emerging AI technologies, establishes scalable governance and engineering practices, and owns measurable business outcomes from enterprise AI solutions. Provides senior leadership coverage for West Coast partners and coordinates with Home Office leadership.

Develop and ship a meaningful engineering project, monitor its production deployment, collaborate with a mentor and manager, test and document work, and present results to the engineering organization. The role may involve machine learning, backend or frontend development, APIs, data processing, deployment, and testing using technologies such as Python, Java, JavaScript, AWS, AngularJS, or React.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Build and lead production machine learning and evaluation systems for Atlassian’s Rovo AI agents. Responsibilities include developing datasets, simulations, automated judges, experimentation platforms, and quality signals; advancing reasoning, planning, retrieval, memory, and tool use; identifying and correcting systemic failures; and balancing quality, safety, latency, reliability, and cost. The role requires hands-on technical leadership, cross-functional influence, architectural decision-making, mentorship, and delivery of measurable customer outcomes at scale.

Data Science Interns collaborate with product and engineering teams to analyze data, build models, support A/B tests and causal inference studies, maintain analytical datasets and pipelines, and communicate actionable insights. The role involves solving complex business problems with senior data scientists and applying responsible generative AI to improve workflow efficiency, cost, and quality. This is a 12-week summer 2027 internship with quarterly in-person work sessions.

Lead the Ranking & Relevance team of software and machine-learning engineers. Own retrieval, ranking, recommendation, and personalization systems that match patients with therapists. Drive the transition from filter-based matching to outcome-aware machine learning, establish shared marketplace objectives, improve experimentation and model monitoring, and build evaluation and drift-detection capabilities. Hire and develop engineers, set technical standards, and partner with product, data science, payer, and provider engineering teams in a sensitive healthcare domain.

Build, deploy, scale, and maintain machine learning models and infrastructure for production use. Responsibilities include designing ML solutions, optimizing models and data pipelines, operating distributed systems and cloud platforms, automating testing and deployment, monitoring production models, and applying responsible AI and software development practices. The role collaborates with Product, Data Science, and Agile engineering teams.

Develop and operationalize machine learning and statistical solutions for pharmaceutical development and manufacturing. Responsibilities include predictive modeling, computer vision, exploratory analysis, model validation, deployment, monitoring, software package development, testing, documentation, and model governance. The role translates scientific and business challenges into scalable applications, collaborates with multidisciplinary teams, supports AI/ML projects, and communicates findings to scientific and cross-functional stakeholders.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Develop experiments, causal inference analyses, exploratory data analyses, and machine learning models for Uber’s fares platform. Refine ambiguous business questions, generate hypotheses, influence product strategy, and collaborate with product, engineering, operations, and other teams. Communicate findings to technical and executive audiences while developing AI agents to automate data science workflows.