Train, deploy, and continuously improve your own models on an integrated compute, training, inference, and sandbox stack. Backed by Menlo.
About the role
You will work on reinforcement learning for agents that reason over long horizons, use tools, coordinate with other agents, recover from failure, and improve from experience. This is Prime's broad, cross-domain Applied Research role, focused on RL and ML systems. Your work should hold up across several application domains: algorithms, environments, harnesses, reward and credit-assignment methods, evaluation, and scalable experimental systems. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Relevant experience: hands-on RL training, scalable ML systems, agent environments, evaluation, inference/training infrastructure, or research crossing methods and systems
- 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
More about this role
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,...
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