Top Machine Learning Jobs in San Francisco Bay Area, CA

Build, deploy, scale, and operate production machine learning models and platforms. Responsibilities include developing ML components, distributed data pipelines, cloud and Kubernetes infrastructure, model monitoring and retraining, CI/CD automation, responsible AI governance, and collaboration with product, data science, and engineering teams.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role collaborates with Product and Data Science teams and uses Python, Java, Scala, or related languages.

Develop advanced algorithms and apply statistical, data mining, machine learning, and AI techniques to large structured and unstructured datasets. Identify trends, evaluate emerging data technologies, and communicate analytical findings to clients and ZS stakeholders. The role requires collaboration across multiple projects, strong quantitative and research skills, and proficiency with Python, SQL, Pandas, and PySpark. Client-facing work may require travel.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Build, deploy, scale, and maintain machine learning models and platforms in production. Develop optimized data pipelines, distributed ML systems, cloud-based architectures, and automated CI/CD workflows. Collaborate with product and data science teams, monitor and retrain models, apply responsible AI practices, and ensure secure, governed software delivery. The role requires extensive experience with ML frameworks, distributed systems, cloud services, Kubernetes, and production machine learning operations.

Build, deploy, and maintain production machine learning models and infrastructure. Design ML components, optimize algorithms, create data pipelines, operate distributed systems, and manage cloud-based containerized services. Apply testing, CI/CD, monitoring, model governance, responsible AI, and explainability practices while collaborating with product, data science, and cross-functional engineering teams.

Build, deploy, optimize, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, designing data pipelines, operating distributed systems and cloud platforms, monitoring and retraining models, automating testing and deployment, and applying responsible AI practices. The role collaborates with Product and Data Science teams to deliver scalable solutions using frameworks such as PyTorch or TensorFlow and platforms including AWS, Kubernetes, Spark, and Ray.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include developing ML algorithms, optimizing cloud-based infrastructure, constructing data pipelines, operating distributed systems, automating testing and deployment, monitoring models, and applying responsible and explainable AI practices. The role requires collaboration with product, data science, and cross-functional Agile teams.

Design, build, deploy, retrain, and monitor production machine learning models and components. Develop scalable ML infrastructure, data pipelines, cloud architectures, and distributed systems using Python and related technologies. Collaborate with Product and Data Science teams, automate testing and deployment, apply CI/CD practices, and ensure model governance, security, responsible AI, and explainability across enterprise platforms.

Design and optimize large-scale machine learning model-serving systems, including distributed infrastructure, load balancing, auto-scaling, batching, caching, and inference optimization. Build highly reliable, high-concurrency services serving billions of requests, improve latency and throughput, benchmark inference engines, and develop CI/CD infrastructure for model deployments. Partner with ML engineers to fine-tune and deploy open-source large language models.

Owns Attain’s data science methodology, including causal inference, attribution, incrementality, media mix modeling, audience targeting, and modeled-data validation. Defines defensible measurement standards, partners with Product, Engineering, ML, and commercial teams, presents methodology to clients and partners, manages external consultants, and leads a team of data scientists. The role requires deep AdTech/MarTech experience, strong statistical and ML expertise, executive communication skills, and experience with large-scale consumer data.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and scale machine learning systems for frontier AI data generation and evaluation. Responsibilities include efficient agentic evaluations, model routing, fine-tuning and serving smaller open-weight models, predictive task-difficulty modeling, LLM-as-judge measurement, and productionizing research prototypes. The role requires rigorous experimentation, statistical analysis, strong Python and software engineering, and end-to-end ownership of production ML or software systems.

Advance AI data and agentic environment creation by designing scalable pipelines combining automated generation, real-world data, and expert judgment. Run experiments, evaluate data and model performance, collaborate with engineering, product, customers, and domain experts, and translate research prototypes into production workflows. The role also involves developing expert-authoring workflows, monitoring frontier research, and representing findings through publications, talks, blogs, and customer engagements.

Develops and maintains prompts, responses, guidelines, and datasets for generative AI and large language model tools. Leads labeling initiatives with internal teams and third-party firms, trains stakeholders on LLM and dataset best practices, and supports quality assurance and testing. Requires native-level English, bilingual ability in a specified language, specialized domain knowledge, and a college degree or equivalent experience.

Build core product features across Python backend and TypeScript frontend, develop a WebGPU-based rendering engine and canvas, architect AI agents for design generation and editing, and maintain evaluation and RL pipelines. Work across product, graphics, and AI, owning end-to-end technical problems and shipping high-quality user-facing product features.

Lead data science for a Grant product area, defining metrics, experiments, causal analyses, and decision-making models. Partner with product, engineering, marketing, and business leaders on strategy and roadmaps. Establish data science best practices across Kikoff and help shape AI-agent-enabled analytical workflows. Build production-quality SQL and Python pipelines, analyses, and models while guiding experimentation, monitoring, and policy decisions.

Develop and improve search quality through personalized ranking signals, machine learning models, domain-adapted language models, and LLM-powered search experiences. Responsibilities include designing, training, evaluating, and deploying production-ready models, writing maintainable code, collaborating with customers and cross-functional teams, and mentoring or learning from engineers. The role is hybrid in San Francisco, requiring four office days weekly.

Apply data science and AI techniques to private equity investment activities, including portfolio analytics, machine learning models, market intelligence, deal screening, valuation support, risk assessment, and LP analytics. Collaborate with investment, data engineering, and international teams while developing data sources, financial models, dashboards, and AI-enabled workflows.

Build and deploy LLM-powered features for clinical, patient, and operational workflows. Responsibilities include prompt design, retrieval, tool use, agent orchestration, evaluation harnesses, feedback loops, production operations, and AI engineering tooling. Partner with clinical, product, and safety stakeholders to ensure responsible healthcare-grade AI. Mentor teammates, communicate model behavior clearly, and stay current with emerging AI research and development practices.

Own end-to-end applied machine learning projects that improve marketplace quality and trust. Build pricing-integrity, product-matching, entity-resolution, attribute-extraction, ranking, and violation-detection models using structured and unstructured data. Develop human-in-the-loop review systems, prioritize enforcement actions under operational constraints, evaluate LLMs, and analyze experiments measuring retailer trust and marketplace GMV. Partner cross-functionally with product, engineering, analytics, and marketplace operations to ship models and deliver business impact.

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

Lead the technical vision and strategy for an AI-powered analytics startup. Build the product from architecture through launch, select the technology stack, guide innovation in generative AI and machine learning, and collaborate with the co-founder, investors, and stakeholders. The role combines hands-on software and product development with technical leadership, strategic decision-making, and entrepreneurial execution.

Tenure-track and tenured faculty will teach undergraduate and graduate courses, mentor students and postdoctoral researchers, conduct internationally recognized and independently funded AI or computer science research, and provide university and professional service. Research may focus on physical AI, human-centered AI, AI and software engineering, or AI safety, security, and alignment. Candidates will also contribute to inclusive teaching, experiential learning, and interdisciplinary collaboration across Northeastern’s colleges and research institutes.

Develop and deploy responsible AI solutions that identify, measure, and mitigate bias across Pinterest’s large-scale machine learning applications, including generative AI, search, and recommendation systems. Collaborate with trust and safety, user modeling, content understanding, and engineering teams to adopt fairness tooling and practices. Mentor engineers, contribute to technical strategy, and bridge advanced research with product impact.