Member of Technical Staff - Robot Learning

Warp Sciences builds AI- and robotics-enabled automated laboratory infrastructure to help accelerate medicine discovery. The Member of Technical Staff, Robot Learning, builds and deploys learning systems that enable robots to perform scientific tasks reliably, owning the loop from demonstrations and robot experience through training, evaluation, and deployment.

Responsibilities:

• Develop mid- and post-training methods for robot policies using real-world demonstrations, robot experience, and task-specific data
• Build systems that learn from expert chemists, turning demonstrations of difficult scientific procedures into capabilities robots can reproduce and generalize
• Develop and adapt world models, world-action models, vision-language-action models, and other learned representations for precise, contact-rich manipulation
• Build the training and evaluation systems needed to ingest, process, curate, and learn from large-scale multimodal robot datasets
• Deploy policies on real robotic systems and diagnose failures across models, data, perception, controls, hardware, and task design
• Close the loop between data collection, training, evaluation, and deployment. Determine what data or training intervention is needed, measure whether it works, and use each deployment to improve the next iteration
• Partner closely with chemists, hardware engineers, infrastructure engineers, and robot operators to design tasks, collect useful experience, and turn research advances into dependable laboratory capabilities