Applied Research - Ecosystem & Product

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.

Prime's research compounds when other researchers can run it, inspect it, extend it, and build on it. You will own the path from emerging research artifact to excellent open-source and product experience. This role sits inside Applied Research and works deeply with engineering. You will build core open-source systems, shape researcher-facing features in our training platform, decide which abstractions deserve stable interfaces, and help external contributors ship serious work into the stack.

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  Important open-source RL, environment, evaluation, and agent systems.

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  Researcher-facing features in our training platform, built with engineering and design.

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  APIs, examples, reference implementations, and technical explanations that expose useful abstractions.

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  Work with research users and external contributors that leads to better interfaces, inspectable code, and research artifacts.

We turn a workflow into tasks, environments, evaluations, and reward signals, run post-training experiments against real baselines, and deploy what works. When a result looks good, we find out whether the gain came from the model or from the data, harness, inference, or infrastructure. The reusable parts become shared research, open-source systems, and product capabilities.

Every Applied Research role is externally facing. Most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda.

You will work continuously with external researchers and contributors. Prime's public surface already includes verifiers v1, prime-rl's extensible algorithms layer, and more than 2,500 community environments. The opportunity is to make the full path from task and harness through evaluation, training, and deployment feel like one coherent research system.

• Make a new researcher successful from environment idea to credible evaluation and training run without hiding the underlying experimental choices.

• Turn fast-moving internal agent and RL abstractions into stable public interfaces without freezing weak ideas prematurely.

• Create a contributor path where external researchers can ship environments, harnesses, algorithms, and integrations that meet a serious technical bar.

• Relevant experience: ownership of open-source ML or developer-tool systems, research engineering, technical product work, or communities formed around code and reproducible research.

• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.

• Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, and a willingness to change direction when the evidence changes.

• Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.

• Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them.

We value publications, open-source systems, benchmarks, and shipped products as evidence of exceptional work. Show us something difficult you built, what failed, and how the evidence changed your approach.