Research Engineer RL Infrastructure
AI infrastructure company providing an integrated stack for training, evaluating, deploying, and continuously improving agentic models.
Funding history
Projects
About Prime Intellect
Prime Intellect, Inc. operates AI infrastructure spanning RL environments, hosted training and evaluations, inference, secure sandboxes, and globally sourced GPU compute.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and optimize infrastructure for large-scale reinforcement learning and distributed training. You will improve efficiency across compute, memory, networking, and scheduling; implement low-level performance improvements; develop distributed and asynchronous training systems; contribute to open-source infrastructure; and turn systems bottlenecks into concrete improvements.
Requirements
- Systems engineering experience in AI or machine learning infrastructure
- Large-scale model training or inference experience
- PyTorch
- PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related distributed training frameworks
- Training performance optimization
- Kernel optimization
- Memory optimization
- Communication optimization
- Data parallelism
- Tensor parallelism
- Pipeline parallelism
- GPU architecture
- Profiling
- Performance debugging
Responsibilities
- Build and optimize infrastructure for large-scale reinforcement learning and distributed training
- Improve training efficiency across compute, memory, networking, and scheduling
- Design and implement kernel, communication-path, and runtime performance optimizations
- Develop distributed training systems for data, tensor, and pipeline parallel workloads
- Shape the architecture of asynchronous rollout and post-training systems
- Contribute to open-source libraries and internal training infrastructure
- Translate systems bottlenecks into concrete improvements with researchers and infrastructure engineers
- Stay current with training systems, inference systems, compiler and runtime tooling, and hardware-aware optimization
Benefits
- Equity incentives
- Flexible work arrangements
- Remote work
- In-person office option
- Visa sponsorship
- Relocation assistance
- Quarterly team off-sites
- Hackathons
- Conference attendance
- Learning opportunities