Top AI Ethics Jobs in Seattle, WA

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 enterprise AI tooling lifecycle and company-wide AI learning programs. Manage procurement, deployment, governance, adoption metrics, and change management while partnering with Security, Privacy, Legal, and IT to operationalize responsible AI and scale tooling across the organization.

As Co-Founder and CPTO, you'll lead the development of an explainable AI compliance platform, overseeing architecture, product build, AI model training, customer engagement, and team recruitment.

Lead Responsible AI technical strategy and build scalable, automated evaluation and RAI systems. Own architecture, metrics, and research foundations; partner cross-functionally; manage and mentor a team of data scientists, researchers, and engineers to operationalize adversarial testing, LLM-based evaluation, and defenses against bias and hallucination.

Own the Finance vertical for Agents Data & RL Environments: set product and data strategy, design and scale finance-specific RL simulations and datasets, partner with frontier research teams, and drive cross-functional execution and go-to-market efforts to train and evaluate AI agents on real financial workflows.

The Senior AI Risk Advisor will lead AI risk assessments, develop governance frameworks, evaluate AI vendors, and engage with stakeholders on AI adoption.

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.

Lead enterprise AI strategy, transformation, and governance. Build and scale teams and an internal AI incubator, identify and implement high-value AI use cases, embed AI into operations, ensure responsible and compliant adoption, enable organizational fluency, and influence executive and Board-level decision-making to realize measurable business value.

Design and run rigorous fairness and bias evaluations for AI-assisted People systems across the employee lifecycle. Build scalable testing infrastructure, conduct subgroup and intersectional analyses, investigate disparity sources, recommend mitigations, and translate results for technical and executive stakeholders. Partner with cross-functional teams to establish documentation, monitoring, and validation standards for high-impact human-AI decision systems.

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.