Research Engineer - RL Infrastructure
$150k - $350k • Remote • San Francisco • FullTime
Posted 1d ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Research Engineer
About the job
Prime Intellect is building the open superintelligence stack, providing infrastructure for AI labs. Their platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. They are developing open-source models for long-horizon tasks and the platform their research team uses. The company has raised $150M from prominent investors and AI leaders. They are seeking individuals passionate about building at the intersection of frontier research, infrastructure, and go-to-market for a nascent category.
Responsibilities
- Build and optimize systems infrastructure for large-scale RL and distributed training workloads, contributing to the 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 involving data, tensor, and pipeline parallel workloads.
- 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 translate bottlenecks into systems improvements.
- 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 movement, communication overhead, or parallelization strategy.
- Hands-on experience with large-scale training techniques like 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.
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