Top AI Ethics Jobs in San Francisco Bay Area, CA

Supports AI governance projects by assessing risks and controls, documenting processes, gathering requirements, analyzing information, and synthesizing findings. Advises clients on regulatory compliance, risk management, internal controls, and governance improvements. The role involves applying AI/ML governance frameworks such as NIST and ISO, translating technical concepts for stakeholders, collaborating with diverse teams, and developing expertise in corporate governance and risk management.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Leads CDW’s AI advisory consulting portfolio, including strategy, governance, operating models, adoption, prioritization, and roadmaps. Develops consulting methodologies, delivery frameworks, sales enablement, and thought leadership. Advises executives and customers, leads complex presales pursuits, shapes proposals and business cases, oversees delivery quality and risk, evaluates technical feasibility, and mentors consulting and presales teams.

Leads responsible AI engineering for production-grade generative, agentic AI, and data science systems. Defines fairness, explainability, privacy, safety, governance, and human oversight controls across engineering and MLOps/LLMOps pipelines. Architectures multi-cloud AI platforms, establishes technical standards, manages AI risk, and leads large global onshore and offshore teams. Partners with clients on major AI programs, supports sales efforts, and develops responsible AI playbooks, training, and governance capabilities.

Design, train, and evaluate Responsible AI components: fine-tune LLMs (including RLHF), build adversarial agent evaluations and safety mitigations, collaborate with engineering to deploy mitigations at scale, monitor operational performance, and troubleshoot end-to-end issues.

Leads enterprise AI assurance and audit engagements covering Generative AI, LLMs, AI governance, Responsible AI, risk management, and AI-enabled processes. Evaluates controls for bias, fairness, transparency, privacy, hallucinations, misuse, human oversight, monitoring, and regulatory compliance. Advises senior management and the Board, identifies control gaps and emerging risks, develops audit plans, manages teams and resources, and delivers objective recommendations across the organization.

Drive strategy, roadmaps, and cross-functional programs for AI safety measurement and evaluations. Partner with researchers, data scientists, engineers, policy teams, and product stakeholders to define safety outcomes, improve measurement platforms, integrate AI safety research, establish success criteria, resolve technical tradeoffs, and communicate quantitative progress to senior leadership.

Build scalable AI safety systems for Grok’s multimodal media generation, including real-time moderation, risk detection, safeguards, monitoring, evaluation frameworks, and feedback loops. The role addresses harmful content, bias, deepfakes, intellectual property, and misuse while maintaining performance and low latency. Responsibilities span prototyping through deployment, incident response, and refinement, requiring collaboration with engineering, research, product, and policy teams.

Leads enterprise AI adoption, support, administration, training, governance, and risk management. Administers platforms such as Copilot and Claude, manages licensing and integrations, develops policies and guidance, partners on AI-enabled engineering workflows, pilots automations, monitors usage and costs, and supervises or mentors support staff.

Research Engineer focused on AI safety for conversational agents. Responsibilities include developing safeguards against prompt injection, unsafe tool use, data disclosure, policy violations, and hallucinations; building adversarial evaluations, red-team datasets, classifiers, judges, reward signals, and runtime protections; analyzing production incidents; and partnering across teams to deploy scalable, reliable safeguards.