Job description

OWN YOUR INTELLIGENCE 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. Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet. ROLE IMPACT The next frontier in AI will not be unlocked by models alone. It will be unlocked by systems that let those models train faster, adapt continuously, and operate across real environments at scale. That infrastructure does not exist yet in the form the world needs. We’re building it. RESPONSIBILITIES - Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads by contributing to our prime-rl github.com 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. REQUIREMENTS - 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 (optional) - 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. BENEFITS AND PERKS - Cash Compensation Range of $150-350k, including 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. If you’re excited about building the systems foundation for frontier-scale RL and open superintelligence, we’d love to hear from you.

Requirements

Must-haves

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.
The next frontier in AI will not be unlocked by models alone. It will be unlocked by systems that let those models train faster, adapt continuously, and operate across real environments at scale.
That infrastructure does not exist yet in the form the world needs.
We’re building it.
Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads by contributing to our prime-rl https://github.com/PrimeIntellect-ai/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.
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 (optional)
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.

Nice-to-haves

None flagged.

Application prep

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