Top AI Ethics Jobs in San Francisco Bay Area, CA

Lead UX research to define and scale evaluation of Notion's AI experiences. Create reusable rubrics and measurement approaches, run longitudinal and feature-specific studies, identify failure modes and recovery behaviors, and operationalize evaluation with product, design, engineering, and data science partners to improve model output quality and end-to-end user experience.

Lead development and implementation of AI legal and compliance frameworks. Conduct cross-functional AI reviews, maintain governance docs (model cards, impact assessments), partner with engineering on transparency and fairness requirements, build incident response and monitoring processes, and scale AI legal review workflows.

Lead and scale GM's enterprise Privacy and AI governance operations: build workflows, training, metrics, reporting, and documentation; operationalize AI impact assessments and governance platform; drive continuous improvement, automation, and cross-functional stakeholder alignment to strengthen privacy and responsible AI program maturity.

Lead ownership of reliability, evaluation, and safety for an AI copilot in a trading platform. Build eval systems, benchmarks, automated quality gates, monitoring, and model improvement loops; partner with engineering and product to define safe tool contracts and ensure predictable, auditable assistant behavior in trading workflows.

Lead strategy and architecture for the enterprise knowledge/context layer powering AI: set standards, control models, connector strategy, and federated operating models. Ensure content is AI-ready, permissions-aware, and measurable for retrieval and grounding. Partner with IT, Engineering, Legal, Privacy, and Security to define eligibility, sensitivity, and lifecycle policies and drive adoption across domains.

Provide legal guidance on Rubrik's AI use and products, ensuring compliance with laws and contracts, drafting and negotiating AI terms, coordinating generative AI review processes, educating employees, and improving AI review controls and workflows across cross-functional teams.

Design and deliver custom AI-driven legal workflows, implement and scale post-sales solutions, define KPIs and success metrics, inform product strategy, partner with Customer Success to demonstrate ROI, and maintain objective, ethics-compliant processes and playbooks for plaintiff law firms.

Lead intake, roadmap, and governance for an AI automation/platform team. Harden and ship business-built AI apps to production with secure GCP platforms, define acceptance criteria and responsible-AI gates, manage vendor buy-vs-build decisions, drive adoption, measure outcomes, and manage AI economics and stakeholder communication.

Lead vision, strategy, and roadmap for an AI Evaluation & Learning platform. Build reusable capabilities for evaluation, feedback, auditability, observability, and continuous learning. Define evaluation standards and success metrics, partner with engineering and data science, drive adoption across product teams, and mentor product managers to improve AI quality, reliability, and trust at scale.

Drive cross-functional technical programs to design, integrate, and deploy AI safeguards across model, platform, API, and cloud environments. Translate safety objectives into execution plans, own milestones and risks, coordinate engineering and policy partners, define metrics, and ensure readiness for sensitive deployments while balancing safety, usefulness, and operational constraints.