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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 made available to every ambitious AI team. The company's platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale. Prime Intellect trains open frontier models and ships the same stack to customers, spanning training, deployment, and continuous improvement of models.
As a Research Engineer on the RL Infrastructure team, you will own the systems infrastructure behind large-scale RL and distributed training workloads. You'll contribute to prime-rl, the company's open-source framework, and drive end-to-end training efficiency across compute, memory, networking, and scheduling layers. Your work will include designing and implementing low-level performance optimizations (kernels, communication paths, runtime improvements), working on distributed training systems spanning data, tensor, and pipeline parallel workloads, and helping shape the architecture of the RL training stack, including async rollout and post-training systems. You'll collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements, stay at the frontier of training systems and compiler/runtime tooling, and contribute to open-source libraries used for frontier-scale model training.
Prime Intellect has raised $150M from Founders Fund, Radical Ventures, NVIDIA, and exceptional operators including Andrej Karpathy, Dwarkesh Patel, and leaders from Ramp, Perplexity, Harvey, Cognition, OpenAI, Databricks, and others.
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 (PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related)
- 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
