Machine Learning Intern

REVEL is an AI robotics company developing physical intelligence for general-purpose humanoid robots. We capture the force, dexterity and intent of human work with our Neural Gambit wearable, and use it to train RAI, the intelligence that powers our robots. REVEL is headquartered in Palo Alto, California, with R&D and engineering facilities in Prague and Hradec Králové, Czech Republic. This role is on-site with our engineering team in the Czech Republic.

Support the RAI team on data and training.

• Help build datasets and run experiments

• Tooling and evaluation

• Learn the stack fast

• 1+ years of relevant professional experience

• Python and some ML

• Hungry to learn

• Reliable

• PyTorch

• Robotics

• Work That Ships: We capture how skilled humans work, their force, touch, and judgment, and our robots do the work. You put robots on a paying customer's floor, not in a demo loop

• The Team: Colleagues from NVIDIA, SpaceX, and Neura Robotics, and founders you work with directly. No layers, no process between you and the decisions

• Your Own Hardware: A top-spec GPU workstation, cluster access, and hands-on time with the robots you're building. Not a software sandbox

• Keep Learning: Conference budget for select roles (GTC, CoRL, ICRA), and room to publish and contribute to open source where our IP allows

• Equity for Key Roles: For select positions, meaningful stock options mean you're not just working here; you own a piece of the outcome

• Unlimited Paid Time Off: Real flexibility to take time away when you need it. We trust our people to own their work, their time, and their results

• Prague, On-Site: Robots need hands, so we work together in our Prague lab. Moving here? We sponsor your work visa and cover relocation

• Lunch, On Us: Complimentary lunch every working day, plus coffee, snacks, and drinks whenever you need a boost

• Apply through the link on this posting. Include a short description of an environment you administered or took over: its size, what you changed about how access or devices were managed, and what you would do differently now.