Top AI Ethics Jobs in Boston, MA

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

The AI Governance Analyst drafts AI governance policies, evaluates risks associated with AI systems, collaborates across functions, monitors compliance, and contributes to ongoing research in AI regulations.

Lead and improve machine learning systems for safety in personalization, develop backend services, and partner across teams to address safety challenges.

The role involves implementing AI safety strategies, building evaluation frameworks, and developing safeguards against scientific risks from AI models. Candidates must possess strong technical knowledge and communicate complex concepts effectively.

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.

Review text prompts and AI-generated videos to identify mismatches, visual or behavioral errors, and tag incorrect prompt text. Add concise annotations, perform self-QA, participate in calibration, document recurring model failures, and meet throughput and quality targets using structured annotation platforms.

Support AI/GenAI risk assessments, evaluate cloud and IT controls, contribute to model governance and documentation, and translate technical design into risk and control implications. Collaborate with engineering, data science, cybersecurity, and audit teams to produce client deliverables and strengthen AI, data, and cloud compliance programs.

The Director of Artificial Intelligence will define, lead, and implement AI strategies, managing R&D, building teams, and ensuring ethical AI use to drive business outcomes.

Develop and operate a system for argumentation related to AI safety, utilizing ontologies and knowledge graphs while collaborating with various stakeholders.

Lead internal AI product strategy and operating model: set priorities for knowledge/context layers, reusable skills/agents, governance and trust, adoption and L&D strategy, partner with engineering, security, legal, and stakeholders to scale safe, company-wide AI practices and measure impact.