Founding Scientist, Embodied AI & Robot Learning

GRAM; San Francisco (CA); On-site; USD 225,000 - 300,000; Full time

Job summary

GRAM is seeking a lead researcher in robot-learning to own the research agenda and set technical direction for reusable embodied intelligence. You will collaborate with founders to shape architecture, data, and compute tradeoffs, and mentor growing teams across multiple embodiments and tasks.

Based on-site in San Francisco, you will design representations, objectives, and training methods that transfer across robot morphologies and tools, and deploy models through C++ runtimes with rigorous

Qualifications

• PhD or equivalent research record demonstrated by publications, research systems, or deployed capabilities.
• Led a consequential technical direction in robot learning, reinforcement learning, imitation learning, or embodied foundation models.
• Strong Python and PyTorch or JAX skills, plus working C++ ability for model integration, profiling, and real-time inference.
• Trained an embodied model or policy using a versioned dataset and evaluated it on held-out tasks, environments, embodiments, tools, or agent configurations defined before model selection.
• Deployed a learned model on a physical robot, autonomous vehicle, or other closed-loop physical system; can present measured performance, validation design, and a failure that changed the research direction.

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.

Skills

Python

PyTorch

JAX

C++

Education

PhD in machine learning, robotics, CS, applied mathematics, or related field

Job description

GRAM is seeking a lead researcher in robot-learning to own the research agenda and set technical direction for reusable embodied intelligence. You will collaborate with founders to shape architecture, data, and compute tradeoffs, and mentor growing teams across multiple embodiments and tasks.

Based on-site in San Francisco, you will design representations, objectives, and training methods that transfer across robot morphologies and tools, and deploy models through C++ runtimes with rigorous