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Salary: USD 150,000 - 300,000 / annual
Prime Intellect is building an open superintelligence stack—infrastructure that frontier AI labs build internally, now available to every ambitious AI team. The platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale, from SFT and RL to tool use, agent workflows, and continuously improving production models.
In this Applied Research role, you will own the path from emerging research artifact to excellent open-source and product experience. You sit inside Applied Research and work deeply with engineering to build core open-source systems, shape researcher-facing features in the training platform, decide which abstractions deserve stable interfaces, and help external contributors ship serious 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 what works. 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.
Representative early problems include: making a new researcher successful from environment idea to credible evaluation and training run without hiding underlying experimental choices; turning fast-moving internal agent and RL abstractions into stable public interfaces without freezing weak ideas prematurely; and creating a contributor path where external researchers can ship environments, harnesses, algorithms, and integrations that meet a serious technical bar.
You will own important open-source RL, environment, evaluation, and agent systems; researcher-facing features in the training platform built with engineering and design; APIs, examples, reference implementations, and technical explanations that expose useful abstractions; and 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 willingness to change direction when evidence changes.
- Strong Python and 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 valued: publications, open-source systems, benchmarks, and shipped products. Ability to show something difficult you built, what failed, and how evidence changed your approach.
