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.
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.
Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.
Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.
Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.
Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism.
Strong understanding of GPU architecture, profiling, and performance debugging.
Ability to identify bottlenecks across the stack and drive improvements from first principles.
Comfort working in a fast-moving environment with ambiguous problems and high ownership.
Experience writing or optimizing CUDA / Triton kernels.
Experience with compiler or runtime optimization for ML systems.
Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines.
Experience with multi-node GPU clusters and high-performance networking.
Contributions to open-source ML systems or infrastructure projects.
Interest in publishing technical work or sharing insights through engineering blogs and technical writing.
The next frontier in AI will not be unlocked by models alone. It will be unlocked by systems that let those models train faster, adapt continuously, and operate across real environments at scale.
That infrastructure does not exist yet in the form the world needs.
We’re building it.
Competitive compensation, including equity.
Flexible work arrangements, with the option to work remotely or in person from our San Francisco office.
Visa sponsorship and relocation support for international candidates.
Quarterly team offsites, hackathons, conferences, and learning opportunities.
A deeply technical, high-agency team working on infrastructure for open superintelligence.
If you’re excited about building the systems foundation for frontier-scale RL and open superintelligence, we’d love to hear from you.
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