Top AI & Machine Learning Jobs in San Francisco, CA

Leads GitLab’s Product, Sales, and Finance Data Science teams, directing strategy, prioritization, executive partnership, and business-impact programs. Manages managers and senior individual contributors, develops organizational standards for experimentation, causal inference, predictive modeling, and AI, and builds a high-performing data science culture. The role connects analytics and data science to product adoption, revenue growth, consumption, and executive decision-making while partnering with data and analytics engineering teams.

Leads GitLab’s Product Data Science team of six, setting its vision, roadmap, priorities, and analytical standards. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages hiring, coaching, retention, performance, and team culture while contributing hands-on to analyses, queries, and model reviews. Requires strong product judgment, executive communication, statistical expertise, SQL, Python, machine learning, and experience managing data science teams.

Partner with Sales, Revenue Operations, and Finance to analyze go-to-market performance. Develop production machine learning models for propensity, churn, expansion, and lead scoring; design experiments; conduct causal and statistical analysis; build agentic analytics workflows; contribute to GitLab’s semantic layer; and write production-grade Python and SQL. Communicate insights to nontechnical stakeholders and support executive reporting on consumption, ARR, activation, conversion, retention, and revenue performance.

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.

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.

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 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.