Top AI Ethics Jobs in San Francisco Bay Area, CA

Lead product strategy for GenAI safety across Pinterest: build proactive safety frameworks, run threat modeling and red-teaming, translate policy into product requirements, define safety metrics, manage incident response, and collaborate cross-functionally to ensure equitable, scalable AI safeguards.

Lead design and ownership of end-to-end quality systems for Snorkel's Data-as-a-Service. Establish standards, metrics, governance, and automation to measure and scale contributor, datapoint, and dataset quality. Partner cross-functionally to operationalize quality, then build and grow the Quality team while representing Snorkel externally.

The Frontier Data Lead - RL will manage RL environment projects, ensuring design quality and overseeing teams to meet client specifications in AI labs.

Lead and execute research to improve AI safety and reliability, design experiments, train models, and collaborate on publications.

Lead internal AI product strategy and operating model: set priorities for knowledge/context layers, reusable skills/agents, governance and trust, adoption and L&D strategy, partner with engineering, security, legal, and stakeholders to scale safe, company-wide AI practices and measure impact.

Own security and compliance for the Enterprise AI program by building security tooling, policy-as-code guardrails, reusable secure AI components, and observability to detect anomalous model behavior while partnering with Cyber Security and delivery teams.

Design HCI standards for Agentic AI products: create transparency and human-in-the-loop workflows, dynamic/generative and multi-modal interfaces, trust/error UX for hallucinations, prototype autonomous sequences with tools like Figma/Protopie, and organize dense information for auditability and oversight.

Own decision quality by evaluating AI-generated decisions, identifying reasoning failures, designing feedback and training loops, defining decision-quality metrics, and collaborating with Product, R&D, and Engineering to improve robustness, explainability, and drift detection.

Lead design and implementation of AI and data governance frameworks, privacy guardrails, metadata standards, and compliance with GDPR/HIPAA/CCPA/NIST AI RMF. Monitor and audit models for bias, explainability, and transparency; partner with engineers, stewards, and leaders to embed ethical decision-making, set KPIs, run audits, and produce dashboards and reports to drive governance adoption.

Lead client-facing organizational change for AI and Workspace transformations: design change strategies, engage executives, build training and champion networks, run Gemini pilots, measure adoption, and sustain new behaviors across enterprises.