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Salary: USD 150,000 - 350,000 / annual
Prime Intellect is building the open superintelligence stack—infrastructure that frontier AI labs build internally, now available to every ambitious AI team. The company's platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into a full-stack system for post-training at frontier scale, spanning SFT and RL to tool use, agent workflows, and continuously improving production models. Prime Intellect has raised $150M from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI and infrastructure operators including Andrej Karpathy, Dwarkesh Patel, and leaders from Ramp, Perplexity, Harvey, Cognition, OpenAI, Databricks, and others.
As a Research Engineer on the RL Infrastructure team, you will own the systems infrastructure behind large-scale RL and distributed training workloads, contributing to the prime-rl framework. Your responsibilities include improving end-to-end training efficiency across compute, memory, networking, and scheduling layers; designing and implementing low-level performance optimizations including kernels, communication paths, and runtime improvements; working on distributed training systems spanning data, tensor, and pipeline parallel workloads; helping shape the architecture of the RL training stack including async rollout and post-training systems; contributing to open-source libraries and internal infrastructure for frontier-scale model training; collaborating closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements; and staying at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.
This is a high-impact role at the intersection of frontier research and real infrastructure, where you'll work on systems that enable models to train faster, adapt continuously, and operate across real environments at scale.
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 (optional):
- 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
