[Hiring] Applied Research - Scientific AI @Prime Intellect

Role Description

You will build and post-train agents for scientific research: 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.

Responsibilities

• Every Applied Research role is externally facing, working directly with customers, users, or research partners while maintaining a substantive research agenda.
• 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

• 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.
• Evidence of exceptional work, including publications, open-source systems, benchmarks, and shipped products.

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