Top AI & Machine Learning Jobs in San Francisco, CA

Ownership of reliability, monitoring, and incident response for AI infrastructure. Build automation and tooling for CI/CD and Kubernetes, extend deployment frameworks, integrate security tooling, improve observability and documentation, and develop internal full-stack tools in Go or Python. Provide on-call support, lead RCA and blameless retros, and drive measurable reliability improvements.

Lead end-to-end pricing experimentation and strategy for consumer and business products: design A/B tests, build pricing and predictive models, develop production-grade data pipelines, partner with Product/Engineering/Finance, and present actionable recommendations to senior leadership.

Founding engineer building an AI-native finance automation platform: design LLM-based agents for close, reconciliation, and filings; ensure SOX-compliant, auditable agent workflows; integrate NetSuite/Workday/Anaplan and financial data pipelines; define compliance-grade engineering patterns and partner with FP&A/Treasury to translate spreadsheet workflows into production systems.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, create grounding and measurement pipelines, drive end-to-end AI projects, mentor engineers, and participate in on-call and incident response.

Build and operate Coinbase's LLM and agent platform: unified model gateway, agent runtime, knowledge bases, AI governance and FinOps, observability/evaluation, and partner with product, security, legal, finance, and cloud providers to deliver production AI agents.

Architect and build a production-grade Legal AI platform of LLM-based agents and automated workflows to automate high-volume legal tasks, own institutional client agreement automation, consolidate agent tooling, define make-vs-buy strategy, and partner with legal leadership to translate workflows into scalable engineered systems.

Lead technical programs to build and scale knowledge systems powering self-service, internal tools, and AI-assisted support. Translate strategy into technical specs, APIs, schemas, and delivery plans; manage cross-functional alignment, roadmap tracking, dependency and risk management, and launch-readiness. Drive CMS, metadata, RAG/vector DB, and workflow automation initiatives to improve accuracy, machine-readability, and operational scale while reducing manual work.

Lead CX analytics by building and productionizing revenue-attribution and causal impact models, designing experiments, and deploying LLM-powered NLP classification pipelines. Partner with analytics engineers, program managers, and product teams to create segmentation and behavioral signals, ensure statistical rigor for executive reporting and regulatory defensibility, and translate complex model outputs into actionable business recommendations.