Founding Research Scientist, Robot Learning

GRAM is a self-replication company creating populations of insectoids for the physical economy. The Founding Research Scientist will own the robot-learning research agenda, set technical direction and evaluation standards, develop transferable models and training methods, and help build the research team. The role emphasizes measured transfer to physical systems through scalable experimentation and real-world deployment.

Responsibilities:

• Develop, pretrain, and adapt robot foundation models that acquire reusable physical capabilities from heterogeneous experience
• Set the research roadmap, technical standards, and experimental decision process for GRAM's robot-learning program
• Design representations, objectives, architectures, and adaptation methods that transfer across robot morphology, sensor configuration, task, environment, tool, and machine variation
• Build training curricula from heterogeneous physical and simulated experience, with strict dataset and checkpoint lineage
• Scale experiments in PyTorch or JAX while separating gains from model architecture, objective, data composition, compute, initialization, and evaluation leakage
• Define frozen evaluations and falsifiable capability claims for task transfer, environmental robustness, embodiment transfer, data efficiency, latency, and recovery
• Deploy selected models through C++ robotics runtimes, then use physical failures and offline-to-online discrepancies to determine the next research question
• Help recruit, evaluate, and mentor the researchers and engineers who extend the program