Research Engineer - RL Infrastructure

Prime Intellect

18h • $150,000 - $350,000

About The Position

Prime Intellect is building an open superintelligence stack that gives AI teams access to infrastructure for post-training at frontier scale, including compute, environments, evaluations, secure sandboxes, high-performance training, and deployment. The company trains open frontier models and provides the same full-stack platform to customers.

The Research Engineer will build and improve systems infrastructure for large-scale reinforcement learning and distributed training, contributing to the prime-rl framework. The work spans training efficiency, low-level performance optimization, distributed training, RL stack architecture, and open-source and internal infrastructure. The role involves working closely with researchers and infrastructure engineers to turn performance bottlenecks into concrete improvements.

Requirements

• Strong systems engineering experience in AI/ML infrastructure, especially 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 tools.
• Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.
• Hands-on experience with data, tensor, 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 Haves

• Experience writing or optimizing CUDA or 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.

Responsibilities

• Build and optimize systems infrastructure for large-scale RL and distributed training workloads by contributing to the prime-rl framework.
• 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 for data, tensor, and pipeline parallel workloads.
• Help shape the architecture of the RL training stack, including asynchronous rollout and post-training systems.
• Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.
• Collaborate with researchers and infrastructure engineers to translate bottlenecks into systems improvements.
• Stay current with training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.

Benefits

• Cash compensation range of $150,000–$350,000, including equity incentives.
• Flexible work arrangements, with the option to work remotely or in person at the San Francisco offices.
• Visa sponsorship and relocation assistance for international candidates.
• Quarterly team off-sites, hackathons, conferences, and learning opportunities.
• Opportunity to work with a talented, mission-driven team focused on accelerating science and AI.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed