Applied Research – Scientific AI
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Job Description
Applied Research scientist building and post-training agents for scientific discovery. Developing RL environments, evaluations, and reproducible research infrastructure for Prime Intellect’s open superintelligence platform.
Responsibilities:
- Build and post-train agents for scientific research, including literature search, code execution, domain-tool use, and experiment selection
- Work across autonomous research and computational science with environments and evaluations built around research partners' tools, data, and experimental feedback
- Work directly with customers, users, or research partners while maintaining a substantive research agenda
- Build long-horizon environments for literature search, scientific database queries, code and domain-tool execution, and experiment selection within fixed budgets
- Convert research partners' workflows, datasets, and outcome criteria into trainable tasks, evaluations, and reward signals
- Design verifiers for correctness, novelty, and reproducibility, including checks for label leakage, lookup shortcuts, and unsupported conclusions
- Connect programmatic checks, simulations, domain models, and expert feedback to training; calibrate confidence estimates and define human-review requirements
- Develop RL and multi-agent methods for proposing, testing, and critiquing hypotheses
- Compare methods with strong single-agent baselines at equal compute and evaluate on held-out problems
- Maintain reproducible code, data, logs, and experiment configurations; rerun results, measure noise, perform ablations, and release reusable environments, evaluations, and research code
Requirements:
- Depth in RL, agents, or post-training systems and ability to acquire 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 willingness to change direction when evidence changes
- Strong Python
- Ability to build artifacts that other researchers and engineers can run, inspect, and extend
- Comfort owning ambiguous problems and keeping partners and teammates current
- Evidence of exceptional work through publications, open-source systems, benchmarks, or shipped products
Benefits:
- Equity incentives
- 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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