Top AI Ethics Jobs in Boston, MA

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 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.

Lead development and oversight of AI governance frameworks, policies, controls, and risk/compliance practices. Partner with business, delivery, and technical teams to align governance with project delivery, support SDLC-based project governance, recommend improvements, and communicate governance expectations across the organization.

Design and test challenging prompts based on complex professional or personal workflows. Identify AI failure points by iterating prompts in ChatGPT, then create a comprehensive grading rubric for evaluating model outputs. Participants must share their screen, camera, and microphone during the recorded trial and submit the prompt, failure notes, rubric, and generated output file. Successful participants may be considered for ongoing AI evaluation work.

Analyze generative AI content infringements and develop adversarial prompts to identify model vulnerabilities across hate speech, misinformation, intellectual property, and other abuse areas. Manage multilingual datasets, investigate safety circumvention tactics, oversee projects and quality assurance, and collaborate with engineering, product, and policy teams to improve AI safety strategies.

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.

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.

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.

Lead enterprise AI governance, risk management, and Responsible AI adoption. Oversee AI inventory, policy, risk lifecycle, regulatory compliance, vendor assessments, and integration of governance into business workflows. Partner with Legal, Compliance, Security, PMO, and business units to prioritize AI initiatives, report metrics and ROI, and advise executives and boards.