Top AI & Machine Learning Jobs in San Francisco, CA

Develops advanced analytics and machine learning models for client business challenges, particularly using pharmaceutical and healthcare data. Translates complex analyses into actionable business recommendations, communicates findings to clients, manages analytical workstreams, supports project milestones, builds client relationships, and mentors junior team members.

Design and develop scalable software systems and production LLM solutions using RAG, agentic workflows, PEFT, and guardrails. Own architecture, technical execution, SDLC activities, cloud and Databricks governance, DevOps automation, testing, deployment, and system reliability. Collaborate with cross-functional teams, conduct code reviews, translate business requirements, and guide software design. The role is client-facing and follows a hybrid work model with travel as needed.

Lead ZAIDYN's AI Engineering function to design, build, and ship an enterprise-grade agentic AI platform. Own architecture, integrations (AWS Bedrock/AgentCore), LLMOps/eval frameworks, observability, governance, and team hiring/operations. Ensure production readiness, cost/latency controls, auditability, and cross-functional delivery with product and QA while representing the platform externally.

The AI Engineering Specialist will oversee project tasks, manage expectations, mentor junior members, and design scalable AI systems while working with advanced AI models and ML pipelines.

Lead AI partnership strategy and execution to grow AI Frontier Labs alliances. Own alliance performance, co-sell pipeline, joint GTM and solution development, partner account mapping, enablement, marketing, and executive alignment to scale generative AI adoption and revenue.

Lead architecture, design, and delivery of large-scale GenAI-enabled, distributed systems. Manage and mentor engineering teams, define standards and APIs, integrate LLMs and agentic workflows, ensure production readiness, observability, scalability, and partner cross-functionally with Product, AI/ML, Data Engineering, and DevOps.

Lead client-facing delivery of enterprise data and AI platforms on Azure and Databricks. Drive architecture, lakehouse design, scalable ETL/ELT pipelines, data governance, and Agentic AI solutions. Manage cross-functional teams, stakeholder alignment, agile delivery, and AI strategy/governance while contributing to thought leadership and reusable accelerators.