Top AI Ethics Jobs in Seattle, WA

Manage and operationalize Responsible AI (RAI) programs across product and model development, automate RAI processes, coordinate cross-functional reviews (legal, privacy, research, design), perform RAI reviews, and drive RAI operational excellence and community engagement to ensure safe, compliant deployments.

As part of the Safeguards Data Science team, you will analyze user behavior, define success metrics, and foster a data-driven culture to improve AI safety.

Serve as a strategic advisor to executive leadership on AI infrastructure, cloud and HPC strategy. Evaluate emerging AI technologies and infrastructure investments, support technical due diligence for partnerships and M&A, develop executive presentations and decision frameworks, and advise on governance, responsible AI, data privacy, and regulatory trends. Collaborate cross-functionally to translate business priorities into actionable technology strategies and assess risks, scalability, and long-term operational economics.

Perform adversarial testing on language models and multimodal agents, implement guardrails and real-time filters for autonomous tools, and develop constitutional AI principles and RLHF alignment pipelines to ensure safe AI deployment.

Lead execution of AI product roadmaps by translating business needs into sprint-ready AI requirements, managing backlogs, coordinating cross-functional teams, tracking AI metrics, and ensuring responsible AI governance and ethical implementation throughout the product lifecycle.

Review, evaluate, and annotate AI-generated text, images, audio, and video; perform quality checks for accuracy and guideline compliance; identify edge cases and inconsistencies; contribute to dataset creation and calibration; provide feedback to improve annotation workflows and guidelines.

Lead enterprise-wide AI adoption by translating technical AI concepts for non-technical stakeholders, curating education and communications, partnering with change management and L&D, triaging requests, and building a team to drive measurable AI adoption across the organization.

Create clear, structured rater guidelines, evaluation rubrics, and reviewer instructions for human evaluators across domains. Review and revise documentation to remove ambiguity, design scoring frameworks, handle edge cases, and collaborate with researchers and SMEs to ensure consistent, high-quality AI evaluations.

Design and build clinical-grade automated evaluation pipelines (LLM-as-judge + human review), define benchmarks, run rigorous experiments, partner with engineering to implement CI gates for accuracy, factuality, and safety, and publish findings to improve model robustness and reduce hallucinations.

Lead enterprise AI governance, risk management, and Responsible AI adoption. Oversee AI inventory, policy, risk lifecycle, regulatory compliance, vendor assessments, and integration of governance into business workflows. Partner with Legal, Compliance, Security, PMO, and business units to prioritize AI initiatives, report metrics and ROI, and advise executives and boards.