Applied Research - Ecosystem & Product

Prime Intellect, Inc.; New York (NY); Hybrid; USD 150,000 - 300,000; Full time

Benefits offered by this job

Flexible Work Locations

Professional Development budget

Equity incentives

Job summary

Prime Intellect is building the open superintelligence stack, unifying compute, environments, evaluations and secure sandboxes for frontier AI research and deployment. You will own the path from emerging research artifact to an excellent open-source and product experience, working closely with engineering inside Applied Research to ship stable interfaces and contributor-friendly APIs.

This externally facing role involves collaborating with researchers and partners to evolve research-driven

Qualifications

• Strong Python and the ability to build artifacts that researchers and engineers can run, inspect, and extend.
• 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.
• Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them.

Responsibilities

• Own open-source ML or developer-tool systems and related research engineering tasks.
• Develop researcher-facing features in the training platform, with engineering and design collaboration.
• Define APIs, examples, reference implementations, and documentation exposing useful abstractions.
• Work with research users and external contributors to improve interfaces and reproducibility.
• Engage with customers or partners to ship solid research artifacts and platforms.

Skills

Python

Experimental judgment

Research engineering

Artifact creation

Ambiguity tolerance

Job description

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.

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.

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.

Evidence of exceptional work

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.

Benefits and Perks

Cash Compensation Range of $150-300k + equity incentives

Flexible Work (San Francisco, New York, London, or remote)

Professional Development budget

If you're excited about the opportunity to build the foundation for the future of decentralized AI and create a platform that empowers developers and researchers to push the boundaries of what's possible, we'd love to hear from you.