Research Engineer - RL Infrastructure
$150k - $350k • Remote • San Francisco • FullTime
Posted 1d ago
About the job
Prime Intellect is building the open superintelligence stack, providing infrastructure for AI labs. Their platform, Lab, unifies compute, environments, evaluations, and training/deployment for post-training at scale, including SFT, RL, and agent workflows. They are developing open-source models for long-horizon tasks and the platform their research team uses. The company has raised significant funding and is looking for individuals passionate about frontier research, infrastructure, and go-to-market strategies in a nascent category.
Responsibilities
- Build and optimize systems infrastructure for large-scale RL and distributed training workloads using the prime-rl framework.
- Improve end-to-end training efficiency across compute, memory, networking, and scheduling.
- Design and implement low-level performance optimizations for kernels, communication paths, and runtimes.
- Work on distributed training systems involving data, tensor, and pipeline parallelism.
- Shape the architecture of the RL training stack, including async rollout and post-training systems.
- Contribute to open-source libraries and internal infrastructure for frontier-scale model training.
- Collaborate with researchers and infrastructure engineers to address bottlenecks.
- Stay current with training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.
Requirements
- Strong systems engineering experience in AI/ML infrastructure, particularly large-scale model training or inference.
- Deep familiarity with PyTorch and distributed training frameworks (e.g., PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray).
- Experience optimizing training performance across kernels, memory, communication, or parallelization.
- Hands-on experience with large-scale training techniques like data, tensor, and pipeline parallelism.
- Strong understanding of GPU architecture, profiling, and performance debugging.
- Ability to identify and resolve bottlenecks across the stack from first principles.
- Comfort working in a fast-paced, ambiguous environment with high ownership.
Benefits
- Cash Compensation Range of $150-350k
- Equity incentives
- Flexible work arrangements (remote or in-person)
- Visa sponsorship
- Relocation assistance
- Quarterly team off-sites, hackathons, conferences, and learning opportunities