Research Engineer - RL Infrastructure
About The Position
Prime Intellect is building an open superintelligence stack that gives AI teams access to infrastructure for frontier-scale post-training, including compute, environments, evaluations, secure sandboxes, high-performance training, and deployment. The company trains open frontier models and provides customers with the same full-stack platform for training, deploying, and continuously improving models. The Research Engineer - RL Infrastructure will build and improve systems that enable large-scale reinforcement learning and distributed training. The role focuses on making training faster and more efficient across compute, memory, networking, scheduling, and runtime layers; shaping the architecture of the RL training stack; and contributing to open-source and internal infrastructure. The engineer will work closely with researchers and infrastructure engineers to identify bottlenecks and deliver systems 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 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 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 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 spanning 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 concrete 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, hard-working, mission-driven team.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Education Level
No Education Listed
