# Applied Researcher - Ecosystem & Product at Prime Intellect

> Remote · United States · Unspecified · Other · open. Source: https://alderhq.app/jobs/ashby%3APrimeIntellect%3Ad45f07a8-a506-4e54-8640-23ad76cc9a2e (read 2026-11-04).

## Facts (observed)

- Company: [Prime Intellect](https://alderhq.app/companies/prime-intellect)
- Location text: San Francisco; New York; Remote; London
- Where it can be done from (inferred): Remote · United States (evidence: "Location: San Francisco; New York; Remote; London")
- Posted: 2026-10-19 (16 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/d45f07a8-a506-4e54-8640-23ad76cc9a2e

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 1737): p25 $106k, median $163k, p75 $219k

## 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.

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.

### Role Impact

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.

### Responsibilities

### Applied Research at Prime

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.

### Role scope

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.

### Representative early problems

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.

### What you will own

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

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

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

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

### Requirements

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

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3APrimeIntellect%3Ad45f07a8-a506-4e54-8640-23ad76cc9a2e and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.