Top AI Ethics Jobs in Seattle, WA

Sell AI services and SaaS to new and existing B2B clients across industries, build pipeline, manage accounts, record CRM data, present complex proposals, and explain AI regulations and standards (e.g., 42001, 42006). Maintain client relationships and ensure timely delivery and retention.

Develop and teach one module of an AI Team Management course for managers. Collaborate with learning designers to shape lessons, scenarios, exercises, videos, and a hands-on playbook. Record engaging instructional videos, explain AI adoption and leadership concepts accessibly, review course materials, and help learners build practical team AI adoption plans, review protocols, operating norms, and 90-day implementation strategies.

Lead customer-facing engagements to design, deploy, govern, and drive adoption of Microsoft 365 Copilot, AI agents, and cloud solutions. Advise on identity, security, compliance, governance, and responsible AI; develop adoption and change-management strategies; analyze usage and business impact; and coordinate partners to scale solutions.

Lead development and execution of MVB's AI and automation strategy, including Generative and Agentic AI and RPA. Partner with business and technical stakeholders to design, pilot, and scale solutions, establish governance and best practices, monitor performance metrics, manage vendors, and deliver training to ensure ethical, secure, and effective AI deployments.

Design, implement, and evaluate applied research projects to improve alignment, safety, and evaluation of LLM-based guardrails. Work on experiments (e.g., synthetic data quality metrics, explanations for blocked content), deliver results with mentor guidance, and present findings to the team to shape product capabilities.

Perform human review and annotation tasks to train and evaluate AI: assess prompts and outputs, correct text, analyze multimedia, record voice samples, and provide structured annotations and culturally informed feedback following guidelines.

Conduct red teaming and adversarial evaluations of AI systems across violence, extremism, child safety, grooming, crisis, and other harm categories. Design evaluation frameworks, test scenarios, scoring criteria, taxonomies, and interventions that translate real-world harm expertise into structured technical outputs. Review model responses, exercise expert judgment, and produce methodology and findings for technical and government audiences while meeting ethical, legal, contractual, and data protection obligations.

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.

Audits AI-generated support responses for accuracy, completeness, hallucinations, compliance, and customer trust. Governs knowledge sources, identifies outdated or conflicting content, performs root-cause analysis, and builds performance dashboards. Partners with Content, Product, Engineering, and Support teams to improve prompts, workflows, guardrails, documentation, and AI training. Establishes scalable QA frameworks and closed-loop optimization processes for AI support experiences.

Lead enterprise adoption of AI by translating strategy into an executable roadmap, building governance and operating models, driving cross-functional program execution, defining success metrics and business cases, and advising executives to ensure responsible, measurable AI impact across clinical and operational domains.