Applied Researcher - Scientific AI

Prime Intellect is building the open superintelligence stack, including its Lab platform for post-training at frontier scale (SFT, RL, tool use, agent workflows, and production models). As an Applied Researcher, you will build and post-train agents for scientific research, including searching literature, writing and running code, using domain tools, and choosing the next experiment. The work spans autonomous research and computational science, with environments and evaluations built around research partners' tools, data, and experimental feedback. Every Applied Research role is externally facing, and most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda.

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, and test whether agents use the underlying evidence; Connect programmatic checks, simulations, domain models, and expert feedback to training, calibrate confidence estimates, and define where an experiment needs human review; Develop RL and multi-agent methods for proposing, testing, and critiquing hypotheses, and compare them with strong single-agent baselines at equal compute; Keep code, data, logs, and experiment configurations reproducible, and release reusable environments, evaluations, and research code

Requirements

Depth in RL, agents, or post-training systems and the ability to acquire the relevant scientific context; 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

Nice to have

Prior work in autonomous research, ML for science, scientific computing, or reproducible computational research; Publications, open-source systems, benchmarks, or shipped products as evidence of exceptional work

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