Research Engineer - Distributed Training

About the Position

Research Engineer - Distributed Training

We are looking for a Research Engineer specializing in Distributed Training to join Prime Intellect. You will be instrumental in building and optimizing the distributed training infrastructure behind our pre-training and large-scale RL training workloads. Your work will contribute to our prime-rl framework and help shape the architecture of our RL training stack.

Responsibilities

• 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.

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.

We Offer

• Cash Compensation Range of $150-350k, including 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.

About the Company

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them.

If you’re excited about building the systems foundation for frontier-scale training and open superintelligence, we’d love to hear from you.