Applied Research - Ecosystem & Product

Prime Intellect•San Francisco, CA

1d•$150,000 - $300,000

About The Position

Prime Intellect is building an open superintelligence stack: infrastructure that brings together compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. The Applied Research - Ecosystem & Product role owns the path from emerging research artifacts to strong open-source and product experiences. It sits within Applied Research and works closely with engineering to build open-source systems, shape researcher-facing training-platform features, establish stable interfaces, and help external contributors deliver work into the stack. Applied Research at Prime turns workflows into tasks, environments, evaluations, and reward signals; runs post-training experiments against real baselines; and deploys successful approaches. The team investigates whether gains came from the model or from the data, harness, inference, or infrastructure, then develops reusable components as shared research, open-source systems, and product capabilities. The role works continuously with external researchers and contributors. It aims to make the path from task and harness through evaluation, training, and deployment a coherent research system while preserving visibility into experimental choices.

Requirements

Relevant experience owning open-source ML or developer-tool systems, research engineering, technical product work, or communities built 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, including meaningful baselines, careful trace inspection, clear failure attribution, and willingness to change direction when evidence changes.
Strong Python skills and ability to build artifacts that other researchers and engineers can run, inspect, and extend.
Comfort owning ambiguous problems and keeping partners and teammates informed while resolving them.
Evidence of exceptional work; examples may include publications, open-source systems, benchmarks, or shipped products. Applicants are encouraged to show something difficult they built, what failed, and how evidence changed their approach.

Responsibilities

Build and own important open-source RL, environment, evaluation, and agent systems.
Develop researcher-facing features in the training platform in collaboration with engineering and design.
Define APIs, examples, reference implementations, and technical explanations that expose useful abstractions.
Work with research users and external contributors to improve interfaces, inspectable code, and research artifacts.
Help researchers progress from an environment idea to a credible evaluation and training run without concealing experimental choices.
Turn evolving internal agent and RL abstractions into stable public interfaces while avoiding premature commitment to weak ideas.
Create a contributor path for external researchers to ship environments, harnesses, algorithms, and integrations that meet a serious technical bar.
Run post-training experiments against real baselines and assess whether results derive from the model, data, harness, inference, or infrastructure.
Work directly with customers, users, or research partners while maintaining a substantive research agenda.