Research Engineer - RL Infrastructure

BUILDING OPEN SUPERINTELLIGENCE INFRASTRUCTURE

Prime Intellect is building the open superintelligence stack: from frontier agentic models to the infrastructure that enables anyone to train, adapt, and deploy them.

We unify globally distributed compute into a single control plane and pair it with the full reinforcement learning post-training stack: environments, secure sandboxes, verifiable evaluations, and our async RL trainer. We enable researchers, startups, and enterprises to run end-to-end RL at frontier scale, adapting models to real tools, workflows, and deployment environments.

We are looking for a Research Engineer to work on the systems layer behind large-scale RL training. This role is for someone who enjoys going deep on performance: optimizing kernels, improving memory and communication efficiency, scaling distributed workloads, and pushing the throughput and reliability of training systems closer to hardware limits.

If you care about making large-scale model training faster, cheaper, and more robust, we’d love to talk.

WHAT YOU’LL WORK ON

- Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads.

- Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers.

- Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.

- Work on distributed training systems spanning data, tensor, and pipeline parallel workloads.

- Help shape the architecture of our RL training stack, including async rollout and post-training systems.

- Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.

- Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.

- Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques....