Top AI Ethics Jobs in Seattle, WA

Own the Responsible AI evaluation framework, evaluation program, and AI transparency standard. Define product vision, strategy, roadmap, features, user stories, and backlogs for AI evaluation tests and benchmarks. Partner with Legal, Privacy, AI engineering, and business stakeholders to prioritize evaluations, coordinate releases, monitor quality, and close findings. The role requires technical experience, product management expertise, Agile SDLC knowledge, and familiarity with Responsible AI frameworks and regulated AI applications.

Lead applied research and engineering for feedback-driven agentic AI: define reward modeling and reinforcement learning roadmap, design production learning loops and evaluation frameworks, run large-scale experiments, and ship research into production while partnering across product and engineering.

Lead product strategy and roadmap for AI-driven content experiences across Edge, Windows, Copilot and partner surfaces. Build scalable trust-and-safety systems, define policies for compliance and risk, partner with policy, engineering, data science, legal and operations, and drive cross-functional execution and metrics to reduce harm and maintain content quality at scale.

Leads responsible AI assessments and governance programs for high-impact AI products and use cases. Evaluates risks, architectures, mitigation effectiveness, and product releases; develops scalable policies and requirements; manages cross-functional programs; supports incident response; and coordinates engineering, research, sales, policy, and marketing stakeholders to advance safe and responsible AI deployment.

Develops product risk assessment frameworks and leads safety reviews for new AI features. Evaluates technical and nontechnical harms, misuse, unintended consequences, and harmful outputs; collaborates with product, engineering, legal, policy, and safeguards stakeholders to design mitigations. Creates bespoke evaluations, develops responsible AI policy recommendations, tracks emerging risks, prioritizes safety resources, and reports product policy risks and mitigations to senior leadership.

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.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Designs enterprise data and AI governance operating models, risk-tiering frameworks, lifecycle stage gates, and practical controls aligned with major AI governance standards and regulations. Uses Microsoft Purview and Fabric to implement governance, privacy, security, and insider-risk controls. Partners with sales, marketing, and practice leaders to shape offerings, lead client engagements, and develop the consulting team.

Leads the company’s AI governance program by developing frameworks, policies, controls, inventories, approval processes, and monitoring for AI and LLM tools. Partners with security, engineering, IT operations, risk, legal, and compliance teams to manage AI risks, third-party due diligence, data protection, regulatory compliance, audits, training, and responsible AI adoption. Tracks emerging regulations and standards while measuring program effectiveness and driving continuous improvement.

Leads trust and safety for AI products serving teachers and students. Owns model guardrails, evaluations, red-teaming, monitoring, incident response, student-facing safety, district trust documentation, and regulatory alignment. Builds safety infrastructure into product development, manages incidents and postmortems, partners with Legal and Engineering, and leads a growing Trust & Safety team.

Reviews AI model and vendor decisions for policy adherence, accuracy, and consistency. Creates and maintains golden sets, calibrates vendor sites, manages vendor relationships, analyzes quality metrics, and improves policies, workflows, and tools. Collaborates with operations, policy, engineering, and leadership teams while engaging with potentially objectionable content.