Top AI Ethics Jobs in Boston, MA

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, and quality of explanations. Provide clear, opinionated feedback, distinguish response quality levels, and help define best practices for AI-assisted developer workflows.

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.

Lead firmwide AI enablement by setting strategy and roadmap, designing multi-level training, running an AI champion community, producing adoption analytics, shaping governance, evaluating tools and vendors, and managing stakeholders and a small team to drive responsible AI adoption and measurable business impact.

Review, evaluate, and annotate AI-generated text, images, audio, and video; perform quality checks for accuracy and guideline compliance; identify edge cases and inconsistencies; contribute to dataset creation and calibration; provide feedback to improve annotation workflows and guidelines.

The Enterprise Architect for Data & AI will shape the architecture for the SaaS platform, ensuring efficient data practices and responsible AI governance, while collaborating with various teams to align technology with business goals.

The Software Architect will lead AI quality evaluation, design evaluation frameworks, create risk controls, and ensure compliance, partnering with various teams on AI systems.

Design, build, and govern a centralized knowledge foundation for enterprise AI. Audit, structure, and optimize content for retrieval and AI use; define taxonomy and content standards; capture institutional knowledge; partner with stakeholders to keep knowledge current and reusable; improve retrieval quality and governance for downstream AI applications.

Lead design and scaling of legal AI evaluation frameworks and benchmarks, build and maintain datasets, audit and score AI legal outputs, define metrics focused on reasoning and citation accuracy, and collaborate with engineering to translate legal errors into model fine-tuning feedback.

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.

Design, implement, and optimize explainable clinical reasoning AI systems that integrate patient data and medical knowledge. Build RAG/embedding pipelines, backend integrations, scalable Docker/cloud-native deployments, and ensure transparent, evidence-backed model outputs while collaborating with cross-functional teams.