Top AI Ethics Jobs in Seattle, WA

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.

Execute AI use-case, vendor, model, agentic AI, and generative AI risk assessments; administer Credo AI workflows; develop governance processes, risk classifications, controls, and reporting; evaluate AI contracts; monitor regulatory frameworks; and advise governance committees while partnering with Legal, Cybersecurity, Procurement, Engineering, and business stakeholders.

Leads enterprise AI, data, automation, and emerging technology strategy. Owns data architecture, master data governance, data quality, privacy controls, AI governance, self-service analytics, automation standards, tooling, budgets, vendors, and a global team. Builds a data and AI center of excellence, establishes policies and training, delivers automation use cases, and partners with security, legal, compliance, architecture, and business leaders to scale responsible technology adoption.

Evaluate and improve AI-generated outputs for accuracy, clarity, quality, and business alignment. Develop and refine prompts, annotate data, conduct rubric-based assessments, fact-check content, and provide actionable feedback on model performance. Create and edit technical documentation, strategy reports, market analyses, business cases, executive summaries, and professional communications. The role requires strong analytical reasoning, writing, research, quality assurance, and attention to detail.

Develops and maintains AI governance policies, frameworks, and controls aligned with regulatory and industry standards. Conducts AI model risk assessments covering bias, explainability, data quality, and security; evaluates organizational and vendor risks; supports audits; designs controls; and partners with Legal, Privacy, Security, and Technology teams. The role also promotes compliant AI practices and organizational awareness while applying advanced AI/ML knowledge.

Lead AiLO portfolio integrating AI into K-12 systems: set strategy, design and draft high-stakes deliverables, manage complex projects, mentor teams, cultivate partnerships, and represent ILO externally to scale responsible AI adoption in education.

Supports responsible AI governance by developing frameworks, conducting fairness and risk assessments, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight practices. Works across technical, legal, compliance, risk, and business teams to promote ethical, transparent, accountable, and trustworthy AI systems.

Evaluate AI models for dangerous CBRNE-related outputs, designing adversarial prompts, multi-turn attack chains, and threat models. Assess technical accuracy and real-world risk, distinguish open-source information from harmful uplift, score responses using severity rubrics, and document failures. Collaborate with AI researchers and policy teams to improve model safeguards and evaluation frameworks while following strict ethical and operational security protocols.

Leads research and thought leadership on AI-led go-to-market strategy and business transformation for small and midsize technology providers. Advises CEOs and executive teams on revenue architecture, sales, marketing, customer success, partnerships, pricing, operations, and converging AI technology stacks. Conducts market analysis, predicts trends, presents recommendations to clients and conferences, supports sales, reviews research, mentors colleagues, and improves research processes.

Manage the AI opportunity roadmap for Risk Services, prioritizing use cases, coordinating internal data science teams and external vendors, translating business needs into requirements, and supporting AI governance, compliance, explainability, auditability, implementation, adoption, and benefits realization. The role focuses on business outcomes and solution delivery rather than hands-on model development.