Top AI Ethics Jobs in Boston, MA

Develops and oversees enterprise AI governance policies, standards, controls, monitoring, and risk-management processes. Advises business and technology leaders on responsible AI, regulatory requirements, controls, and mitigation strategies. Coordinates with cybersecurity, privacy, legal, compliance, architecture, and engineering teams; evaluates control effectiveness; drives corrective actions; supports audits, executive reporting, and governance committees; and promotes adoption through stakeholder education. Requires 8+ years in data analysis, reporting, or related fields, with SQL, analytics, data modeling, and visualization experience.

Advise clients on privacy, data protection, and responsible AI requirements. Evaluate AI systems for fairness, transparency, safety, privacy, accountability, and third-party risk. Develop governance operating models, policies, risk-tiering approaches, controls, impact assessments, and client deliverables. Interpret regulatory changes, support breach investigations, collaborate with legal, compliance, cybersecurity, data, and technology teams, and mentor junior staff. Apply technical literacy in AI/ML, GenAI, software development, technical architecture, and Python.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Leads CDW’s AI advisory consulting portfolio, including strategy, governance, operating models, adoption, prioritization, and roadmaps. Develops consulting methodologies, delivery frameworks, sales enablement, and thought leadership. Advises executives and customers, leads complex presales pursuits, shapes proposals and business cases, oversees delivery quality and risk, evaluates technical feasibility, and mentors consulting and presales teams.

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 enterprise-wide data and AI governance strategy, including policies, standards, stewardship, data quality, analytics platforms, responsible AI, security, privacy, compliance, and model lifecycle management. Builds and manages governance, data science, and analytics teams while partnering with IT, legal, compliance, and business leaders. Evaluates governance technologies, advances data-driven decision-making, and promotes ethical, secure, transparent, and accountable use of data and AI.

Leads the Open Source Program Office and AI Governance Program, establishing licensing compliance, responsible AI frameworks, policies, controls, risk management, and reporting. Oversees governance analysts and engineering teams developing automation, infrastructure, and compliance tooling. Partners with Legal, Security, Product, Engineering, Sales, and executives to manage open-source and AI risks, enable adoption, and maximize business value. Provides strategic direction, team leadership, and executive reporting on governance effectiveness and compliance posture.

Develop and implement safety mitigations for frontier scientific AI models, agents, tools, and laboratory-connected workflows. Translate risk assessments and policy requirements into safeguards spanning training, classifiers, monitoring, access controls, and runtime interventions. Design experiments, evaluate robustness and legitimate-use impacts, investigate failures and distribution shifts, and integrate mitigations with model training, inference, platform, and evaluation teams.

Leads clinical AI strategy, validation, safety evaluation, and deployment for clinical decision-support solutions. Partners with Product, Engineering, Editorial, Business Development, customers, and strategic partners to ensure clinically accurate and trustworthy implementations. Designs clinical outcomes studies, translates stakeholder feedback into improvements, monitors AI and regulatory developments, and represents the organization through thought leadership. Physicians may maintain up to 20% clinical practice.

Lead regional execution of AI capability and enablement: run coaching, triage, experimentation, and workflows; track adoption metrics; provide hands-on AI coaching and prototyping; synthesize recurring patterns into reusable assets; coordinate cross-functional stakeholders; manage enablement projects and a team of analysts to scale responsible AI usage across the region.