Research Engineer - Distributed Training

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

Job summary

Prime Intellect is building open frontier AI infrastructure, unifying training and deployment to enable frontier-scale models. We seek experts to architect and optimize distributed training systems and contribute to open-source components.

You will collaborate with researchers and infra engineers, optimize performance across compute, memory, and networking, and shape the RL training stack with async rollout and post-training systems.

Qualifications

• Strong systems engineering experience in AI/ML infrastructure.

Responsibilities

• Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads.

Skills

Systems engineering

AI/ML infrastructure

PyTorch

Distributed training

Performance profiling

Tools

PyTorch Distributed

DeepSpeed

FSDP

Megatron

vLLM

Ray

CUDA

Triton

Job description

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

We train open frontier models and ship the same stack to our customers. Its spans the full stack of training, deploying and continuously improving models — compute, large-scale RL, environments, sandboxes, evals, and deployment.

What You’ll Work On

Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL 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.

You May Be a Fit If You Have

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.

Especially Exciting

Experience writing or optimizing CUDA / Triton kernels.

Experience with compiler or runtime optimization for ML systems.

Experience working on 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.

Cash Compensation Range of $150-350k, plus equity incentives, aligning your success with the growth and impact of Prime Intellect.

Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.

Visa sponsorship and relocation assistance for international candidates.

Quarterly team off-sites, hackathons, conferences and learning opportunities.

Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.