# Research Engineer - Distributed Training at Prime Intellect

> Remote · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3APrimeIntellect%3A8bd52610-175c-42a7-a7cd-b29c45f9d305 (read 2026-11-05).

## Facts (observed)

- Company: [Prime Intellect](https://alderhq.app/companies/prime-intellect)
- Location text: San Francisco; Remote
- Where it can be done from (inferred): Remote · United States (evidence: "Location: San Francisco; Remote")
- Posted: 2026-07-08 (120 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/PrimeIntellect/8bd52610-175c-42a7-a7cd-b29c45f9d305
- Skills listed (inferred): PyTorch

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 397): p25 $200k, median $245k, p75 $300k

## Description (the employer's text)

### 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 https://github.com/PrimeIntellect-ai/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 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.

### 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.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3APrimeIntellect%3A8bd52610-175c-42a7-a7cd-b29c45f9d305 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.