Developer Relations Engineer

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.

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.

You'll help developers build with Model Factory by creating documentation and practical tutorials that take them from a first training run to improving a model on their own tasks.

Working directly with our researchers and engineers, you'll turn the workflows we use at Prime into reproducible examples and help developers understand the open-source foundations of the platform, including prime-rl and Verifiers.

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  Own the Model Factory documentation, giving developers a clear path from their first quickstart to more advanced workflows with reliable API and SDK examples.

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  Build runnable tutorials that follow a model through the improvement cycle, from investigating failures in traces to designing evaluations and training with SFT or RL, with clear comparisons of results before deployment.

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  Work with Research and Applied Research to turn internal workflows into reproducible examples that developers can adapt to their own problems.

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  Partner with Engineering to publish documentation alongside new capabilities and maintain example code as the platform evolves.

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  Observe developers using Model Factory and work with Product and Engineering to resolve the points of confusion, whether they need a better explanation or a change to the product.

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  Create technical content in the format best suited to the subject, including hands-on workshops where developers can work through a training workflow with you.

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  Support developers building on prime-rl and Verifiers, helping them contribute useful environments and examples back to the open-source community.

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  Use tutorial completion and time to a first successful workflow to assess the quality of the developer experience, drawing on recurring support questions and repeat usage to prioritize improvements.

• A public track record of useful technical work and an established, engaged following in the developer or AI community, with evidence that your content or open-source projects helped people adopt a tool or build something.

• Recent hands-on software or ML engineering experience, with strong Python skills and examples of code you personally built and maintained.

• Experience explaining complex technical concepts through documentation or working examples that give developers enough context to reproduce results and understand their limitations.

• A working understanding of LLM training and evaluation, with the ability to learn unfamiliar post-training techniques through practical experimentation.

• The ability to take a documentation or tutorial project from an initial user problem through to a tested release, incorporating feedback from the people using it.

• Experience working closely with engineers or researchers to understand technical details and translate developer feedback into practical improvements.

• Hands-on work with SFT or RL, including the design of evaluations and rewards for agent environments.

• Contributions to open-source ML infrastructure or developer tools.

• Maintaining documentation and example repositories alongside a changing product, including automated checks for broken examples.

• Teaching technical audiences through workshops or hands-on community support.