Top AI Research Jobs in NYC, NY

As a Retrosynthesis Researcher, you'll develop ML models for retrosynthetic pathway prediction, leverage cheminformatics tools, and collaborate with chemists to validate outcomes.

Lead MongoDB's strategy to increase LLM and answer-engine visibility by optimizing content, structured data, and community signals. Build and scale an AEO/SEO team, implement technical information architecture (JSON-LD, knowledge graphs, RAG-friendly docs), coordinate UGC/community advocacy, evangelize AI-first content practices, and develop analytics/attribution (Share of Model, citation impact) to drive PLG acquisition.

Manage and coach a team of mentors to ensure delivery outcomes for ~200 AI fellows at scale. Build delivery systems, run operational cadence, lead live instruction when needed, translate field signals into curriculum changes, and partner with strategic partners to close the feedback loop between mentors, fellows, and the curriculum team.

Lead research on agentic systems, agent ecosystems, advanced agentic capabilities, human-agent interaction, evaluation tooling, and responsible AI. Build and deploy prototypes, collaborate across teams, publish research, and translate innovations into practical, impactful applications.

Lead research on agentic AI including recursive self-improvement, multi-agent coordination, continual learning, autonomous skill acquisition, and human-AI collaboration. Build evaluation frameworks and synthetic data pipelines, refine scalable architectures, and drive research-to-deployment in collaboration with engineers and product teams.

Lead and conduct research on agentic AI, develop training algorithms and datasets for large language and multimodal models, design evaluation frameworks for reasoning and long-horizon tasks, prototype agentic capabilities (action models, memory, multi-agent orchestration), and drive research to publication and production impact in collaboration with engineers and product teams.

Join Research Innovation to build and support AI assistants, agentic applications, and citizen development tooling. Provide user support, design training and workflows, work with engineering on enterprise agentic software, and teach non-technical colleagues. Adapt to shifting priorities and contribute ideas to scale AI tooling across the research organization.

Lead forward-looking research on robot learning, hardware, supply chains, and commercialization. Produce reports, forecasts, and briefings; track mobile manipulators, quadrupeds, cobots, and autonomous vehicles; assess AI, edge compute, and simulation impacts; collaborate with cross-sector analysts; support investors, OEMs, and policy stakeholders; and present findings to senior executives and clients.

Build first-principles performance and cost models for AI training and inference across chips, accelerators, memory, interconnects, racks, and datacenters. Evaluate architectures, validate models against benchmarks, analyze semiconductor manufacturing and packaging impacts on performance and cost, publish research, and advise clients and investors on hardware trade-offs and TCO metrics.

Lead end-to-end AI advisory engagements in a chosen vertical: qualify and sell advisory in pre-sales, run discovery workshops, scope SOWs, deliver final readouts, build vertical POV and playbooks, coordinate technical and design partners, and manage and coach a Consultant. Frequent travel (30–60%) required.

Lead and run AI advisory engagements: conduct market research, impact sizing, synthesis, and workshops; manage timelines and cross-functional coordination; build reusable playbooks and AI-powered automation to accelerate delivery and commercial expansion.

Join a research and advisory pipeline for an asset-management startup building an auditable reasoning engine. Advise on agentic AI, world models, interpretability, and systemic risk. Help shape research direction, identify failure modes and guardrails, and provide scholarly evidence (papers, GitHub, Google Scholar) and a short written response to demonstrate expertise.

Produce and maintain technical and user-facing content about Pangram's AI detection models. Author and edit research papers, technical reports, model cards, benchmarks, and guides. Translate model architecture, training methodology, and statistical evaluation into accurate, accessible explanations for technical and non-technical audiences. Collaborate with research and engineering to ensure documentation accuracy.

Lead enterprise-wide AI learning strategy and programs at Marsh: design, implement, and measure AI literacy and skill-building initiatives; partner with leaders and technical teams; build scalable, persona-based training; embed learning into work; track adoption and ROI; enable leaders and cultivate an AI learning community. Hybrid role based in New York.