Scientific AI Researcher

Key responsibilities: Build long-horizon environments for agents to search literature, query databases, and run experiments within a defined budget Transform research partners' workflows and datasets into trainable tasks and evaluations Design verification methods for correctness and reproducibility, ensuring agents utilize evidence appropriately

Required qualifications: Depth in reinforcement learning (RL), agents, or post-training systems with the ability to acquire scientific context Hands-on experience in building and debugging model-training or evaluation loops Strong Python programming skills to create artifacts for research and engineering use Demonstrated experimental judgment with experience in baseline evaluation and failure attribution Comfort with ambiguous problems and ability to keep partners informed during resolution