# Research Engineer - RL Infrastructure

**Company:** [Prime Intellect](https://hotfix.jobs/companies/prime-intellect)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $150k – $350k/yr
**Skills:** PyTorch, Pytorch Distributed, Deepspeed, Fsdp, Megatron, vLLM, Ray, CUDA, Triton, Gpu Architecture, Distributed Training, Reinforcement Learning, High-Performance Networking, Compiler Optimization, Linux
**Posted:** 2026-07-08

> Build and optimize infrastructure for frontier-scale reinforcement learning and distributed model training, including kernels, runtimes, parallelism, and asynchronous rollouts. The role requires strong AI/ML systems experience, PyTorch expertise, and GPU performance optimization skills.

## Job Description

## Responsibilities
- Build and optimize systems infrastructure for large-scale reinforcement learning and distributed training workloads in 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.
- Develop distributed training systems spanning data, tensor, and pipeline parallel workloads.
- Help shape RL training architecture, including asynchronous 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 turn bottlenecks into systems improvements.

## Requirements
- Strong systems engineering experience in AI/ML infrastructure, particularly large-scale model training or inference.
- Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, or Ray.
- Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategies.
- Hands-on experience with 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.

## Nice to Have
- Experience writing or optimizing CUDA or Triton kernels.
- Compiler or runtime optimization experience for ML systems.
- Experience with 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.

## Compensation and Benefits
- Cash compensation of $150,000–$350,000 plus equity.
- Flexible work arrangements, with the option to work remotely or in person from the San Francisco office.
- Visa sponsorship and relocation support for international candidates.
- Quarterly team offsites, hackathons, conferences, and learning opportunities.

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