Prime Intellect - Applied Research
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.
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. • 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.
Benefits
• Flexible Work (San Francisco, New York, London, or remote) • Visa Sponsorship & relocation support • Professional Development budget • Team Off-sites & conference attendance
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