Prime Intellect - Research Engineer

Remote - San Francisco, California, United States$312k+3w ago

Remote NA Research Engineer Ray CUDA Triton Technical Writing GitHub

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. • 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.

Responsibilities

• Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our prime-rl https://github.com/PrimeIntellect-ai/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.

Benefits

• 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.