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Revel Robotics

Builds data infrastructure for humanoid robots

RAI Reinforcement Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timePrague, CzechiaNo salary listed

About the Job

Requirements

  • At least 3 years of relevant professional experience.
  • A track record of building and training reinforcement learning policies such as Proximal Policy Optimization, Soft Actor-Critic, or offline reinforcement learning, with a deep understanding of how they work rather than only how to call them.
  • Experience applying reinforcement learning to robotics or physical-system control, including personally deploying and validating reinforcement learning policies on real hardware.
  • A strong understanding of sim-to-real transfer, including domain randomization, system identification, latency, actuator dynamics, and common transfer failure modes.
  • Comfort using physics simulators such as Isaac Lab or MuJoCo, or equivalent, to train and stress-test policies.

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.
  • Move research results into production on the robot.

Desired Qualifications

  • Experience with locomotion or legged-robot reinforcement learning.
  • Understanding of robot dynamics, control theory, or whole-body control.
  • Exposure to vision-language-action models or other foundation models applied to robotics.
  • Experience with offline reinforcement learning, imitation learning, or approaches combining demonstrations with interaction.
  • Publications or strong open-source work in reinforcement learning, machine learning, or robotics.

About the Company

Revel Robotics

Builds data infrastructure for humanoid robots

REVEL Robotics builds a data infrastructure and hardware stack to train humanoid robots for real-world tasks. It creates high-fidelity digital twins from partner manufacturers’ tools and appliances and uses NVIDIA Isaac Sim to run large-scale, domain-randomized simulations; its Neural Gambit wearable captures force, dexterity, and intent to train its RAi model and robotic hands. Unlike general robotics vendors, it focuses on translating human skill into scalable machine capability by pairing data, software, and hardware for B2B access to robotics developers. Its goal is to alleviate skilled-labor shortages in manufacturing, skilled trades, and logistics by teaching robots practical, real-world tasks through embodied AI.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Palo Alto, CA
Founded
2025

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