Remote RL Infrastructure Engineer - Frontier AI Stack

Prime Intellect AI; San Francisco (CA); Hybrid; USD 150,000 - 350,000; Full time

Benefits offered by this job

Remote or SF office work option

Visa sponsorship & relocation

Quarterly team offsites

Job summary

Prime Intellect AI is building an open frontier AI stack that enables frontier-scale model training and deployment. The role focuses on designing and optimizing the underlying systems infrastructure for large-scale RL and distributed training workloads.

You will work closely with researchers and infrastructure engineers to push the limits of training performance, kernels, and runtime optimizations, while contributing to open-source projects and internal tooling for frontier-scale models.

Qualifications

• Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.
• Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.
• 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.

Responsibilities

• Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads by contributing to our 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 spanning data, tensor, and pipeline parallel workloads.
• Help shape the architecture of our RL training stack, including async rollout and post-training systems.
• Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.
• Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.
• Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.

Skills

Systems engineering

AI/ML infrastructure

PyTorch

Distributed training

GPU architecture

Tools

DeepSpeed

Megatron

vLLM

Ray

Job description

Prime Intellect AI is building an open frontier AI stack that enables frontier-scale model training and deployment. The role focuses on designing and optimizing the underlying systems infrastructure for large-scale RL and distributed training workloads.

You will work closely with researchers and infrastructure engineers to push the limits of training performance, kernels, and runtime optimizations, while contributing to open-source projects and internal tooling for frontier-scale models.