About REVEL
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.
Design RL and post-training methods to make policies robust and reliable. Our RAI team builds the core intelligence that powers REVEL robots, learning from the force and intent Neural Gambit captures.
Responsibilities
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.
Design RL and post-training methods to make policies robust and reliable. Our RAI team builds the core intelligence that powers REVEL robots, learning from the force and intent Neural Gambit captures.
Responsibilities
- Train and evaluate models on REVEL's human-demonstration data
- Build scalable, reproducible training and evaluation pipelines
- Collaborate across the RAI, hardware and data teams
- Push results from research into production on the robot
- 3+ years of relevant professional experience
- Track record of building and training RL policies such as PPO, SAC, or offline RL, with a deep understanding of how they work—not just how to call them
- Experience applying RL to robotics or physical-system control, including personally deploying and validating RL policies on real hardwaree
- Strong understanding of sim-to-real transfer, including domain randomization, system identification, latency, actuator dynamics, and common transfer failure modes
- Comfortable using physics simulators such as Isaac Lab, MuJoCo, or equivalent to train and stress-test policies
- Locomotion or legged-robot RL experience
- Understanding of robot dynamics, control theory, or whole-body control
- Exposure to VLAs or other foundation models applied to robotics
- Experience with offline RL, imitation learning, or approaches combining demonstrations with interaction
- Publications or strong open-source work in RL, ML, or 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
- Equity for Key Roles: For select positions, meaningful stock options mean you're not just working here, you own a piece of the outcome
- Salary and Quarterly Bonus: Strong base pay plus a quarterly bonus, in a city where it goes further
- 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
- 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
- Prague, On-Site: Robots need hands, so we work together in our Prague lab. Moving here? We sponsor your work visa and cover relocation
- Health and Fitness: Multisport card (from December 2026), extra paid sick days, and an employer pension contribution
- 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.