Research Engineer - Large-Scale RL Training Infrastructure

About the Role

We are seeking a Research Engineer for Large-Scale RL Training Infrastructure to join our innovative team at Prime Intellect. This remote position offers the opportunity to work on cutting-edge AI infrastructure that supports the development of superintelligent systems. If you are passionate about optimizing performance and enhancing the efficiency of large-scale model training, we want to hear from you!

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.

Requirements

• 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.

Nice to Have

• 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.

What We Offer

• 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.