Applied Researcher - Scientific AI

Highlights

• Build agents for scientific research that search literature and run code tools.
• Design long-horizon environments with verifiers for novelty and reproducibility.
• Develop RL and multi-agent methods to propose and critique hypotheses.

What they offer

• Equity incentives
• Professional development budget
• Team off-sites and conference attendance

Ideal candidate

Experimental researcher who thrives on ambiguous, partner-facing problems and turning messy scientific workflows into rigorous, reproducible agent loops. Motivated by open frontier AI and building systems others can run and extend.

Requirements

• Depth in RL, agents, or post-training systems with ability to acquire scientific context
• Hands-on experience building and debugging training, post-training, agent or evaluation loops
• Strong experimental judgment with meaningful baselines and clear failure attribution
• Strong Python and ability to build artifacts others can run, inspect and extend
• Comfort owning ambiguous problems and keeping partners and teammates updated