Applied Research Lead - New York

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.

We are looking for a senior regional player-coach who can establish that group through personally led research and field programs. You will begin close to the work: shape consequential external problems into coherent research programs, lead the hardest early experiments and deployments, and recruit exceptional researchers who want the same combination of frontier methods and real-world responsibility. As the group grows, you will set its research direction and the technical and experimental standards that let it move quickly without losing rigor.

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  A small number of the hardest New York partner and customer research programs.

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  Recruiting and development of the founding New York Applied Research group.

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  Technical standards for environments, evaluations, post-training experiments, failure analysis, and deployed results.

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  Meaningful personal research, cross-program technical direction, and decisions about what becomes shared research or product capability as the team grows.

We turn a workflow into tasks, environments, evaluations, and reward signals, run post-training experiments against real baselines, and deploy what works. When a result looks good, we find out whether the gain came from the model or from the data, harness, inference, or infrastructure. The reusable parts become shared research, open-source systems, and product capabilities.

Every Applied Research role is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda.

This is a New York leadership role within Prime's broader Applied Research function. It requires research taste, technical depth, customer and partner judgment, exceptional recruiting, and the willingness to remain a builder. You will stay directly involved with external program teams while leading technical work, recruiting, and developing the New York group.

• Turn the first portfolio of New York field programs into research agendas with strong owners, comparable evidence, and explicit paths from domain result to shared capability.

• Personally lead a high-stakes program through environment design, baseline, post-training, evaluation, and deployment while establishing the quality bar for the growing team.

• Recruit the founding New York researchers and establish a working rhythm that keeps review rigorous without slowing the team.

• A track record leading high-performing applied-research or research-engineering teams, deep credibility in RL/post-training/agents/evaluation/ML systems, exceptional recruiting judgment, and experience with demanding external technical stakeholders.

• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.

• Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, and a willingness to change direction when the evidence changes.

• Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.

• Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them.

We value publications, open-source systems, benchmarks, and shipped products as evidence of exceptional work. Show us something difficult you built, what failed, and how the evidence changed your approach.