As a Machine Learning Engineer – Robot Learning, you will help develop, evaluate, and deploy learning-based methods for real-world robotic manipulation. You will work at the intersection of machine learning, robotics, and systems engineering, adapting state-of-the-art research into robust, scalable capabilities that run on real hardware.

You will collaborate closely with robotics, autonomy, perception, simulation, and software teams, and you will own ML workflows from model design to deployment and testing on real robots.

Your Responsibilities

• Research, evaluate, and benchmark state-of-the-art robot learning methods (VLA models, diffusion policies, RL, imitation learning, visuomotor models)

• Adapt academic models for practical, real-world deployment on RobCo’s modular robots

• Train and fine-tune ML models using RobCo datasets and simulation data

• Integrate learned policies with perception, control, and robot runtime systems

• Build scalable training, evaluation, and data pipelines in collaboration with infrastructure teams

• Define clear performance metrics and build automated evaluation procedures in simulation and on real hardware

• Analyze model performance, identify regressions, and drive improvements

• Collaborate with robotics and autonomy engineers to ensure safety, reliability, and real-time performance

• Participate in research planning and contribute to technical decisions across the robot learning stack

• Share knowledge, support junior team members, and help shape internal ML best practices