Top AI & Machine Learning Jobs in San Francisco, CA

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Build and operate production AI agents, agentic workflows, MCP servers, and CX applications. Develop Python and TypeScript services, AWS infrastructure, integrations, testing, observability, security controls, and CI/CD pipelines. Partner with Customer Success and other CX teams to prototype solutions, translate workflows into technical plans, and deliver reusable platforms. Mentor engineers and non-engineers, document systems, establish governance, and support reliable handoff and shared ownership.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Lead technical direction for a greenfield team building agentic/LLM-backed systems to reduce SDLC friction. Drive discovery through shipping, apply distributed-systems judgment at scale, mentor engineers, own technical hiring and cross-team alignment, and evaluate correctness and safety of agentic workflows.

Build reliable AI-powered, agentic backend systems to remove friction across the software development lifecycle. Design multi-step orchestration, safety checks, and evaluation tooling; apply distributed-systems judgment; work across GitLab codebases and with a distributed team to productionize solutions that may become customer-facing.