Top AI Ethics Jobs in NYC, NY

Lead intake, roadmap, and governance for an AI automation/platform team. Harden and ship business-built AI apps to production with secure GCP platforms, define acceptance criteria and responsible-AI gates, manage vendor buy-vs-build decisions, drive adoption, measure outcomes, and manage AI economics and stakeholder communication.

Lead integration of cutting-edge RL and agent self-improvement techniques into the Foundry Agent Platform. Design experiments, evaluate and optimize agent performance, collaborate with researchers and engineers, build actionable observability and visualizations, and mentor team members to drive production-quality AI features.

Lead AI-focused workforce development for at-risk populations: act as internal AI SME, assess training needs, curate and validate curricula, partner with external providers, define KPIs with BI, monitor impact, and represent Verizon in external engagements to advance equitable AI upskilling.

Own end-to-end data and RL environment strategy for domain-specific knowledge work. Source and evaluate tasks and vendors, design rewards and QA to prevent reward hacking, run generalization experiments, and partner with RL research and product teams to improve model capabilities.

The AI Governance Lead will develop and oversee Ascot's AI governance framework, ensuring compliance and ethical use of AI across various business areas, while coordinating with legal and risk departments.

Lead delivery of AI governance programs for financial services clients: build policies, controls, operating models, and KPIs; perform assessments, audits, and model reviews; operationalize monitoring and incident management; support sales, offering development, thought leadership, and people management across multidisciplinary teams.

The AI Executive Officer will lead CUNY's AI strategy, ensuring ethical integration across academic and administrative domains while enhancing student success and faculty support.

Lead enterprise Responsible AI and AI Governance by developing frameworks, policies, controls, and evaluation standards. Oversee reviews of AI use cases, track KPIs/KRMs, advise and challenge teams on risk and compliance, engage stakeholders and regulators, and drive continuous improvement and enterprise adoption of ethical, transparent, and compliant AI practices.

Co-found and lead product and engineering for an explainable AI compliance platform: design architecture and security (SOC 2, multi-tenant), ship LLM-powered production features (marketing review, surveillance, trading oversight), own model training, explainability, reliability, customer engagement, fundraising targets, and recruit the founding engineering and applied AI team.

Lead development and operationalization of a clinical reasoning framework for trustworthy clinical AI. Partner with product, engineering, editorial, and design to define decision pathways, evaluation benchmarks, validation protocols, and ongoing expert review. Translate clinical feedback into AI system improvements, represent the team externally, and optionally maintain limited clinical practice.