Top AI & Machine Learning Jobs in San Francisco, CA

Pioneers and scales autonomous agentic software development lifecycle capabilities across GitLab. Defines technical vision, identifies high-impact AI opportunities, leads production-adjacent experiments, designs agent architectures and safety controls, establishes evaluation frameworks, and drives adoption. The role partners across Engineering, Product, Infrastructure, Security, Reliability, and Data and ML teams to deliver secure, observable, resilient agentic workflows, mentor senior engineers, and convert proven internal patterns into customer-facing product capabilities.

Build and maintain the Python-based backend engine for GitLab Duo Chat, including agentic LangGraph flows, the Flow Registry, and Duo Workflow Service. Integrate chat capabilities with GitLab’s Rails monolith and GraphQL API, develop secure and tested high-scale services, optimize performance, review code, address technical debt, and collaborate across product, UX, and frontend teams. Participate in weekday, weekend, and occasional nighttime on-call rotations.

Maintain and consolidate an inventory of AI tools for the field org, track adoption and utilization, manage access requests, support AI data hygiene, create enablement communications and simple reporting, and coordinate with enablement, IT, and field stakeholders to operationalize AI tooling.

Lead the field organization's AI agenda: define enablement roadmaps, identify and scale high-value AI field use cases, drive operational transformation, build field champions, and establish governance for safe, compliant AI adoption.

Lead technical direction and architecture for DAP onboarding and repository/background agent flows. Design, build, and ship autonomous and scheduled agents that act on customer codebases, improve onboarding experience, collaborate across teams, mentor engineers, and solve complex, high-scale engineering problems.

Lead technical direction for a greenfield team building agentic/LLM-based systems to reduce SDLC friction. Design, implement, and evaluate multi-step orchestration, ensure distributed-systems correctness at scale, mentor engineers, collaborate cross-region, and own technical hiring and foundations from discovery through production.

Design and build reliable AI-powered, agentic backend systems that reduce developer friction across the software lifecycle. Scope problems, implement multi-step orchestration with safety checks and recovery, evaluate agent correctness and cost, apply distributed systems judgment, and own greenfield solutions that may evolve into customer-facing capabilities.

Lead product vision and strategy for GitLab's end-to-end software factory, unifying planning, coding, review, and verification workflows. Partner cross-functionally to identify gaps, align priorities, and present recommendations to executives. Drive cohesive AI-powered DevSecOps experiences and help bring the software factory to market.