Research Engineer / Scientist - Reinforcement Learning

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.

Help develop the algorithms and systems used to train Prime Intellect’s open models with reinforcement learning. We’re hiring research engineers and scientists across RL algorithms, scaling research, and training frameworks. Your focus will reflect your strengths; you don’t need to cover all three.

• Develop and test RL methods for reasoning, coding, and long-horizon tasks, including changes to training objectives and policy optimization.

• Investigate stability in asynchronous RL, including policy staleness, trainer/sampler mismatch, and numerical differences between training and rollouts.

• Work with data and evaluation researchers to understand how task selection, rewards, and training data affect what the model learns.

• Design experiments to understand how RL performance and learning dynamics change with model size, training compute, and task horizon.

• Use controlled ablations and evaluations to distinguish learning gains from reward exploitation, evaluation artifacts, or training bugs.

• Turn experimental findings into training recipes, then test whether the improvements hold across larger models and longer runs.

• Extend prime-rl to support new algorithms, models, and asynchronous training workflows.

• Improve the performance and numerical correctness of research runs across trainers, rollout workers, and environments, working with the infrastructure team.

• Build diagnostics that make unstable runs and performance regressions easier to reproduce, and help distinguish algorithm problems from implementation or runtime issues.

• Demonstrated depth in at least one of RL algorithms, empirical research on model training, or distributed ML systems. This could come through research, open-source contributions, or systems you’ve built.

• Strong programming skills in Python and experience with PyTorch or a comparable ML framework.

• Ability to design controlled experiments, interpret results, and debug unexpected model or system behavior.

• Experience taking a research idea or systems change through implementation, testing, and evaluation, with a clear account of what worked and what didn’t.

• Experience with distributed training frameworks or asynchronous rollout pipelines.

• Experience diagnosing unstable RL runs, including policy-staleness or numerical issues.

• Work on coding agents, verifiable rewards, or long-horizon environments.