Frontier AI Training Systems Engineer

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

Benefits offered by this job

Cash compensation and equity Incentive

Remote or SF office

Visa sponsorship and relocation

Team off-sites and learning

Open-source collaboration

Job summary

Prime Intellect AI is building the open superintelligence stack and frontier AI infrastructure. You will help design, implement, and optimize distributed training systems for pre-training and RL workloads, collaborating with researchers and engineers to push the boundaries of model-scale and performance.

You’ll work on kernels, runtimes, and multi-node GPU setups, contributing to open-source ML systems and tooling while enjoying flexible work arrangements—remote or in-person in San

Qualifications

• Strong systems engineering experience in AI/ML infrastructure.
• 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 distributed training infrastructure behind pre-training and large-scale RL training workloads by 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 spanning data, tensor, and pipeline parallel workloads.
• Help shape the architecture of the 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.

Job description

Prime Intellect AI is building the open superintelligence stack and frontier AI infrastructure. You will help design, implement, and optimize distributed training systems for pre-training and RL workloads, collaborating with researchers and engineers to push the boundaries of model-scale and performance.

You’ll work on kernels, runtimes, and multi-node GPU setups, contributing to open-source ML systems and tooling while enjoying flexible work arrangements—remote or in-person in San