Top AI Ethics Jobs in Boston, MA

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.

Evaluate AI-generated responses in English and French (France), performing side-by-side comparisons for accuracy, relevance, reasoning, safety, tone, and usefulness. Apply detailed annotation guidelines, document decisions with concise rationale, meet productivity and quality targets, complete onboarding and qualification, and participate in ongoing calibration and feedback.

Lead strategy and roadmaps to operationalize AI models across the product portfolio. Drive cross-functional execution, ensure Responsible AI and regulatory alignment, support product launches, and evangelize AI outcomes to stakeholders and customers.

Lead complex legal AI programs from strategy through adoption: advise senior legal stakeholders, orchestrate multidisciplinary teams, translate delivery signals into program decisions, create lightweight operating cadence, challenge weak assumptions, and build reusable frameworks and governance to scale AI capabilities across clients.

Senior client-facing advisor who defines AI adoption strategy, leads executive workshops, develops AI roadmaps and business cases, and ensures alignment from strategy to implementation. Partners with sales, architects, and delivery teams to drive responsible AI, governance, data readiness, and measurable value realization.

The Director, Analytics Standards & Impact Measurement oversees AI/ML standards, quantifies analytics impact, and ensures compliance within commercial analytics initiatives.

The role involves evaluating AI outputs, generating content, providing annotations, and ensuring quality through cultural and contextual awareness.

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.

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.

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.