Top AI Ethics Jobs in Boston, MA

Design and deliver custom AI-driven legal workflows, implement and scale post-sales solutions, define KPIs and success metrics, inform product strategy, partner with Customer Success to demonstrate ROI, and maintain objective, ethics-compliant processes and playbooks for plaintiff law firms.

Lead strategy and architecture for the enterprise knowledge/context layer powering AI: set standards, control models, connector strategy, and federated operating models. Ensure content is AI-ready, permissions-aware, and measurable for retrieval and grounding. Partner with IT, Engineering, Legal, Privacy, and Security to define eligibility, sensitivity, and lifecycle policies and drive adoption across domains.

Design, train, and evaluate Responsible AI components: fine-tune LLMs (including RLHF), build adversarial agent evaluations and safety mitigations, collaborate with engineering to deploy mitigations at scale, monitor operational performance, and troubleshoot end-to-end issues.

Leads product safety and wellbeing strategy for AI-powered experiences. Owns safety roadmaps, guardrails, evaluation frameworks, benchmarks, metrics, monitoring, launch-readiness criteria, and human or automated evaluation programs. Assesses failure modes and emerging risks, including self-harm and crisis scenarios, and translates expert and user findings into product guidance. Partners with engineering, machine learning, product, design, and data science teams to improve risk detection, response generation, escalation, and user experience.

Leads the enterprise AI governance program, overseeing AI risk assessments, controls, policies, regulatory alignment, committee governance, and executive reporting. Partners with cybersecurity, privacy, legal, compliance, technology, and business teams to enable responsible AI adoption, strengthen governance processes, document risk decisions, and mature the operating model. The role also leads and mentors teams while serving as a credible challenge partner across the organization.

Architect and implement enterprise AI solutions, enable cross-functional teams in AI adoption, guide technology evaluation, and ensure responsible AI practices.

Design and run adversarial tests on language and multimodal models, build guardrails and real-time filters for autonomous tools, and develop constitutional AI principles and RLHF alignment pipelines to ensure safe AI deployment.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Design and operationalize enterprise AI governance capabilities across the AI lifecycle. Translate policies, privacy, security, risk, and compliance requirements into technical controls, guardrails, automated workflows, monitoring, observability, and audit evidence. Develop AI use-case registration, classification, taxonomy, risk assessment, and shadow-AI management capabilities. Partner with Privacy, Legal, Cybersecurity, Supply Chain, and Enterprise Architecture teams to implement responsible, secure, and compliant AI systems.

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.