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Applied Research - Ecosystem & Product

Prime Intellect· Applied Research
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San Francisco, New York, Remote, London$150k - $300kFull-timePosted 5d ago

About the role

Role focused on turning emerging research into open-source and product-ready systems within Prime Intellect's training platform, working deeply with engineering and external researchers to build stable interfaces, APIs, and contributions for the open frontier AI stack.

Responsibilities

  • Applied Research at Prime: turn a workflow into tasks, environments, evaluations, and reward signals; run post-training experiments against baselines; deploy what works; determine whether gains come from model, data, harness, inference, or infrastructure; convert reusable parts into shared research, open-source systems, and product capabilities.
  • Role scope: work continuously with external researchers and contributors; public surface includes verifiers v1, prime-rl extensible algorithms layer, and 2,500+ community environments; make the full path from task to 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 experimental choices. - Turn fast-moving internal agent/RL abstractions into stable public interfaces without premature freezing. - Create a contributor path for external researchers to ship environments, harnesses, algorithms, and integrations meeting a serious technical bar.
  • What you will own: - Important open-source RL, environment, evaluation, and agent systems. - Researcher-facing features in the training platform (engineering & design). - APIs, examples, reference implementations, and technical explanations exposing useful abstractions. - Work with research users and external contributors to improve interfaces, inspectable code, and research artifacts.

Qualifications

  • Ownership of open-source ML or developer-tool systems, research engineering, technical product work, or communities around reproducible research.
  • Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops.
  • Strong experimental judgment: baselines, trace inspection, clear failure attribution, willingness to pivot with evidence.
  • Strong Python and ability to build artifacts that researchers/engineers can run, inspect, and extend.
  • Comfort owning ambiguous problems and keeping partners and teammates current.

Skills

Required:Open-source ML or developer-tool systemsModel-training / post-training loopsPythonExperimental design and analysis

Benefits

  • Cash Compensation Range of $150-300k + equity incentives
  • Flexible Work (San Francisco, New York, London, or remote)
  • Visa Sponsorship & relocation support
  • Professional Development budget
  • Team Off-sites & conference attendance
Last updated Oct 21, 2026. Interested? Apply on the company site.

About Prime Intellect

Commoditizing Compute & Intelligence

Prime Intellect democratizes AI development at scale. Our platform makes it easy to find global compute resources and train state-of-the-art models through distributed training across clusters. Collectively own the resulting open AI innovations, from language models to scientific breakthroughs.

AI developmentbessemer-ai-infrastructure-2026compute resourcesdistributed trainingmodelsreinforcement-learning-platformsscience
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