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Applied Research - Scientific AI

Prime Intellect· Applied Research
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San Francisco, New York, Remote, Berlin, LondonFull-timePosted 4d ago

About the role

Prime Intellect is building an open superintelligence stack and is seeking researchers to build and post-train agents for scientific research, spanning autonomous research and computational science, with environments and evaluations built around partners’ tools, data, and experimental feedback.

Responsibilities

  • Build long-horizon environments where agents search literature, query scientific databases, run code and domain tools, and choose experiments within a fixed compute or experimental budget.
  • Turn research partners' workflows, datasets, and outcome criteria into trainable tasks, evaluations, and reward signals.
  • Design verifiers for correctness, novelty, and reproducibility. Test whether agents use the underlying evidence, with checks for label leakage, lookup shortcuts, and unsupported conclusions.
  • Connect programmatic checks, simulations, domain models, and expert feedback to training. Account for noisy or delayed results, calibrate confidence estimates, and define where an experiment needs human review.
  • Develop RL and multi-agent methods for proposing, testing, and critiquing hypotheses. Compare them with strong single-agent baselines at equal compute and evaluate on held-out problems.
  • Keep code, data, logs, and experiment configurations reproducible. Re-run promising results, measure noise, and use ablations to separate model improvements from changes to the data, harness, or inference setup. Release reusable environments, evaluations, and research code.

Qualifications

  • We are looking first for depth in RL, agents, or post-training systems and the ability to acquire the relevant scientific context.
  • Prior work in autonomous research, ML for science, scientific computing, or reproducible computational research is useful but not required.
  • 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.

Skills

Required:Reinforcement Learning (RL)Post-training systems / agent developmentPython

Benefits

  • Cash Compensation Range of $150-300k + equity incentives
  • Flexible Work (San Francisco, New York, London, Berlin, 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
Company profileWebsite

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