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About the Role

Prime Intellect is building the open superintelligence stack, providing infrastructure for ambitious AI teams. Our platform unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. We are developing open frontier AI models and the full-stack platform our research team uses to build them. The next generation of AI companies needs the ability to turn their workflows, tools, data, and feedback loops into superintelligence they own. We train open frontier models and ship the same stack to our customers, spanning training, deployment, and continuous improvement of models.

Responsibilities

Build and optimize systems infrastructure for large-scale RL and distributed training workloads using the prime-rl framework.
Improve end-to-end training efficiency across compute, memory, networking, and scheduling.
Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.
Work on distributed training systems for data, tensor, and pipeline parallel workloads.
Shape the architecture of the RL training stack, including async rollout and post-training systems.
Contribute to open-source libraries and internal infrastructure for frontier-scale model training.
Collaborate with researchers and infrastructure engineers to translate bottlenecks into systems improvements.
Stay current with training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.

Requirements

Strong systems engineering experience in AI/ML infrastructure, particularly large-scale model training or inference.
Deep familiarity with PyTorch and distributed training frameworks (e.g., PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray).
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 stack bottlenecks and drive improvements from first principles.
Comfort working in a fast-moving environment with ambiguous problems and high ownership.

Skills

AI/ML infrastructure
Large-scale model training
Large-scale model inference
PyTorch
PyTorch Distributed
DeepSpeed
FSDP
Megatron
vLLM
Ray
Kernel optimization
Memory movement optimization
Communication overhead optimization
Parallelization strategy
Data parallelism
Tensor parallelism
Pipeline parallelism
GPU architecture
Performance profiling
Performance debugging
CUDA
Triton kernels
Compiler optimization
Runtime optimization
RL training infrastructure
Rollout systems
Asynchronous training pipelines
Multi-node GPU clusters
High-performance networking
Open-source ML systems
Open-source ML infrastructure

Location

San Francisco
Remote

Work Type

Remote
In-person

Experience Level

Systems engineering experience
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

Salary/Compensations

$150-350k

Benefits

Cash Compensation Range of $150-350k, including equity incentives
Flexible work arrangements
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

About the Company

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.