Applied Research - Scientific AI

Role Impact:

• Build and post-train agents for scientific research: searching literature, writing and running code, using domain tools, and choosing the next experiment.
• Work spans autonomous research and computational science, with environments and evaluations built around research partners' tools, data, and experimental feedback.
• Maintain a substantive research agenda while working directly with customers, users, or research partners.

Responsibilities:

• Build long-horizon environments where agents search literature, query scientific databases, run code and domain tools, and choose experiments within fixed compute or experimental budgets.
• Turn research partners' workflows, datasets, and outcome criteria into trainable tasks, evaluations, and reward signals; design verifiers for correctness, novelty, and reproducibility.
• Develop RL and multi-agent methods, keep code/data/logs reproducible, and release reusable environments and research code.

Requirements:

• Depth in RL, agents, or post-training systems; hands-on experience building and debugging model-training, post-training, agent, or evaluation 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; evidence of exceptional work via publications, open-source systems, benchmarks, or shipped products.

Benefits and Perks:

• Cash compensation range of $150-300k plus equity incentives.
• Flexible work in San Francisco, New York, London, Berlin, or remote; visa sponsorship and relocation support.
• Professional development budget, team off-sites, and conference attendance.