Top AI & Machine Learning Jobs in San Francisco, CA

Leads applied science strategy for AI-powered products, designing LLM evaluation frameworks, offline experiments, quality standards, and production monitoring. Builds and evaluates agentic, retrieval-augmented, and multimodal AI systems while partnering with engineering and product leaders. Translates customer and business goals into measurable outcomes, drives experimentation and launch decisions, promotes reliability and MLOps practices, mentors technical staff, and communicates findings and recommendations to diverse stakeholders.

Develop machine learning techniques for AI safety, alignment, and trustworthy AI. Collaborate with research, product, and engineering teams to design, build, deploy, monitor, and continuously improve responsible AI systems and LLM-based services at scale.

Defines strategy, roadmap, and platform capabilities for Responsible AI across the AI lifecycle. Leads development of evaluations, red teaming, guardrails, risk detection, monitoring, governance workflows, transparency, and provenance. Partners with engineering, research, security, legal, compliance, and enterprise customers to translate emerging risks and regulations into scalable products. Drives customer discovery, cross-organizational execution, success metrics, adoption, and industry engagement for trustworthy AI capabilities.

Leads responsible AI programs for high-impact models, products, and use cases. Evaluates AI risks, architectures, and mitigation effectiveness; develops scalable policies, governance processes, and requirements; manages cross-functional implementation across engineering, research, sales, and policy teams; supports incident response; and drives accountability for responsible AI throughout product development and release lifecycles.

Design, fine-tune, and evaluate large language models (including RLHF). Build adversarial agent evaluations and safety mitigations, collaborate with engineering to deploy Responsible AI solutions at scale, and troubleshoot end-to-end operational issues.

Design and build large-scale, highly available distributed cloud AI services focused on Responsible AI. Lead cross-functional projects, evaluate architectures, own end-to-end development and production readiness, drive DevOps practices, and integrate with Azure AI offerings to ensure performance, scalability, observability, and customer-focused solutions.

Design, implement, test, and operate production-quality AI framework components, benchmark and optimize large-model training and inference on GPUs and Microsoft silicon, build automation and observability, drive cross-team projects from definition through deployment, and mentor engineers to improve model onboarding, performance, reliability, and platform efficiency.

Designs, builds, and deploys AI agents and enterprise solutions across Microsoft AI Business Solutions and Azure. Partners with Microsoft customers and partners on technical pre-sales, architecture, production readiness, value quantification, and ROI. Creates reference architectures, sample agents, workshops, bootcamps, and enablement content while mentoring architects and facilitating technical sessions. Collaborates with product, field, marketing, and skilling teams to advance partner AI practices and deliver outcome-based transformation solutions.

Leads product analytics strategy across Xbox Console, PC, Store, and Platform experiences. Establishes metrics and measurement frameworks, identifies growth opportunities through advanced analytics and machine learning, directs experimentation and causal inference, builds reusable analytical and AI-enabled capabilities, improves telemetry and measurement infrastructure, and mentors data scientists while influencing senior leaders and portfolio investment decisions.

Leads technical sales engagements for Microsoft cloud and AI solutions. Conducts proofs of concept, hackathons, coding sessions, and architecture workshops to drive adoption of AI Foundry, Azure AI, and Responsible AI. Designs secure, scalable, cloud-native architectures; resolves technical blockers; collaborates with engineering teams; and guides enterprise customers through AI application modernization, deployment workflows, orchestration, and production adoption.