Top Machine Learning Jobs in San Francisco Bay Area, CA

Provide legal guidance on Rubrik's AI use and products, ensuring compliance with laws and contracts, drafting and negotiating AI terms, coordinating generative AI review processes, educating employees, and improving AI review controls and workflows across cross-functional teams.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead and grow a team of ML and software engineers to build large-scale indexing, retrieval, and ranking systems for Snapchat ads. Define architecture and evaluation frameworks, collaborate with product and data science, measure outcomes, and raise engineering and ML standards while shaping Snap's broader ML strategy.

Design, build, and productionize causal ML models (uplift, heterogeneous treatment effects), analyze A/B tests and quasi-experiments, collaborate with product and engineering, ensure scalable, maintainable infrastructure, and balance model complexity with interpretability and business impact.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Design, research, implement, and deploy scalable deep learning, LLM and generative AI solutions (including RAG and fine-tuning). Advance Responsible AI practices, set research agenda, and collaborate with engineers and scientists to improve model capabilities and reliability.

Build and scale Gusto's ML/AI platform: design MLOps solutions, automated pipelines, model development frameworks, deployment, monitoring, CI/CD, and observability; collaborate with ML engineers and apply AI tools and best practices to production ML systems.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Lead product strategy and execution for enterprise AI/ML and Generative/Agentic AI platforms. Drive capabilities for model training, serving, and developer/data scientist UX, partner across platforms, and deliver business value and adoption at scale.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

In this role, you will design and deploy advanced machine learning and Generative AI solutions, leading projects from conception to deployment while mentoring junior team members.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.

Build and improve ML systems powering core AI capabilities: create and maintain evaluation datasets, design experiments, train and fine-tune models (including RL approaches), analyze results to drive product decisions, participate in research, and partner with product and backend teams to integrate models into production.

You will lead AI deployments within top pharmaceutical and biotech firms, act as a strategic advisor, and translate customer needs into product requirements.

The CTO will lead Fathom's technical strategy, focusing on AI governance, building partnerships, defining standards, and advancing AI safety practices.

Lead the development and integration of advanced safety systems for scientific AI applications, building a team and establishing a research strategy.

The AI Lab Research Engineer will develop scientific AI models for complex problems, design and deploy AI agents, and collaborate across disciplines in a lab environment.

Lead and mentor a team of Solution Architects, ensuring technical solutions align with customer needs, while managing deployments and proofs of concept for AI applications.

As a Research Engineer, you will develop evaluation frameworks for AI models and agents, ensuring reliable metrics for model performance and product readiness.

The Data Scientist will build AI-driven fraud and risk systems, work with real-world data, and leverage graph techniques to improve detection and labeling. They will partner with engineering to scale data systems in the compliance domain.

Lead and build an in-house data annotation team, define processes and tooling, coordinate with ML and engineering, create documentation, manage annotators, and perform annotations when needed.