ML Research Scientist (Embodied AI & Reinforcement Learning)

What you'll do

• Design, train, and evaluate methods including world models, action models, reinforcement learning, and learning from demonstration.
• Lead research work end-to-end, including problem formulation, large-scale experimentation, and evaluation.
• Apply neuroscience principles to embodied-agent representations, dynamics, and behaviors; collaborate on deployment and evaluation in real-world settings.

What they ask for

• PhD in Machine Learning, Robotics, Computer Science, Computational Neuroscience, or a related field—or equivalent research experience demonstrated through publications and shipped systems.
• Strong publication record or technical track record in reinforcement learning, world models, action models, imitation learning, or embodied AI.
• Deep working knowledge of modern deep learning, including large-scale training, distributed optimization, and rigorous evaluation methodology.
• Strong software engineering skills, proficiency in Python and a modern deep learning framework, and hands-on experience training and evaluating policies in simulation.

Nice to have

• Experience deploying learned policies on real robotic systems, including bimanual or humanoid platforms.
• Background in computational or systems neuroscience, or experience using neural data to constrain or evaluate machine learning models.

Pay

$175,000 - $250,000 USD (base salary)

Tools

Python

AI focus

Research and build embodied AI methods and neuro-aligned agents, including reinforcement learning, world and action models, learning from demonstration, and evaluation in simulation and the real world; use neuroscience principles to shape agent learning and behavior.