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Salary: USD 150,000 - 300,000 / annual
Prime Intellect is building the open superintelligence stack—infrastructure for frontier AI labs to conduct post-training at scale. The company has raised $150M from Founders Fund, Radical Ventures, NVIDIA, and prominent AI operators including Andrej Karpathy and leaders from Perplexity, Harvey, Cognition, OpenAI, and others.
You will establish and lead Prime Intellect's New York Applied Research group as a senior player-coach. This is a leadership role that combines hands-on research with team building and external stakeholder management. You will shape customer and partner problems into coherent research programs, personally lead early high-stakes experiments and deployments, and recruit exceptional researchers to build out the group.
Applied Research at Prime Intellect turns workflows into tasks, environments, evaluations, and reward signals. You will run post-training experiments against real baselines, deploy what works, and determine whether gains come from the model, data, harness, inference, or infrastructure. Reusable results become shared research, open-source systems, and product capabilities.
Key responsibilities include: converting the initial New York field programs into research agendas with strong owners and clear paths from domain results to shared capabilities; personally leading high-stakes programs through environment design, baseline establishment, post-training, evaluation, and deployment while setting quality standards; recruiting the founding New York researchers and establishing a working rhythm that maintains rigor without slowing velocity; owning a small number of the hardest partner and customer research programs; recruiting and developing the founding Applied Research group; setting technical standards for environments, evaluations, post-training experiments, failure analysis, and deployed results; and driving meaningful personal research and cross-program technical direction.
Every Applied Research role is externally facing. You will maintain direct involvement with external program teams while leading technical work and team development.
REQUIREMENTS:
- Track record leading high-performing applied-research or research-engineering teams
- Deep credibility in RL, post-training, agents, evaluation, or 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 willingness to change direction based on evidence
- Strong Python proficiency and ability to build artifacts that other researchers and engineers can run, inspect, and extend
- Comfort owning ambiguous problems and keeping partners and teammates informed as you resolve them
- Evidence of exceptional work: publications, open-source systems, benchmarks, or shipped products demonstrating difficult work, failures, and how evidence shaped your approach
