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Prime Intellect
San Francisco, United States4 months ago
150,000 – 350,000 USD
per year
Work format
Remote, Onsite
English level
B2, upper-intermediate
Relocation
United States, San Francisco
An email or a Telegram message to the recruiter
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, Semianalysis, 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
Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our prime-rl framework.
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
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
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
Especially Exciting (optional)
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
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, Semianalysis, 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
Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our prime-rl framework.
RESPONSIBILITIES
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.
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 training and open superintelligence, we’d love to hear from you.
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