What You'll Work On- Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our 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 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.
You May Be a Fit If You Have- 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.
Especially Exciting- 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.
Benefits & Perks- Cash Compensation Range of $150-350k, plus equity incentives, aligning your success with the growth and impact of Prime Intellect.
- Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.
- 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 and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.
If you're excited about building the systems foundation for frontier-scale training and open superintelligence, we'd love to hear from you.
