Asks for

The ad says“Strong Python and C++ skills for robot-facing and performance-critical code”
The ad says“Experience optimizing models for real-time inference using ONNX, TensorRT, quantization, compilation, or similar tools”
The ad says“Experience integrating and deploying learned models or control policies on real robots”
The ad says“Hands-on experience with ROS 2, DDS, NVIDIA Jetson, and robot hardware interfaces”
The ad says“Have helped or deployed policies from simulation onto real robots, using techniques like domain or dynamics randomization, and know the ways that transfer tends to break”
The ad says“Have helped or deployed policies from simulation onto real robots, using techniques like domain or dynamics randomization, and know the ways that transfer tends to break”
The ad says“Worked directly with robotics physics simulators: Isaac Sim/IsaacLab, Newton, MuJoCo, or similar, at a level deep enough to reason about what the simulator is actually computing, not just how to call it”
The ad says“Worked directly with robotics physics simulators: Isaac Sim/IsaacLab, Newton, MuJoCo, or similar, at a level deep enough to reason about what the simulator is actually computing, not just how to call it”
The ad says“Worked directly with robotics physics simulators: Isaac Sim/IsaacLab, Newton, MuJoCo, or similar, at a level deep enough to reason about what the simulator is actually computing, not just how to call it”
The ad says“Worked directly with robotics physics simulators: Isaac Sim/IsaacLab, Newton, MuJoCo, or similar, at a level deep enough to reason about what the simulator is actually computing, not just how to call it”
The ad says“Hands-on experience with ROS 2, DDS, NVIDIA Jetson, and robot hardware interfaces”
The ad says“Experience optimizing models for real-time inference using ONNX, TensorRT, quantization, compilation, or similar tools”
The ad says“Comfortable in Python, and genuinely fluent in the physics underneath: how rigid bodies interact, how contact gets modeled, how actuators behave — this isn't a black box to you”
The ad says“Experience optimizing models for real-time inference using ONNX, TensorRT, quantization, compilation, or similar tools”
The ad says“Experience optimizing models for real-time inference using ONNX, TensorRT, quantization, compilation, or similar tools”
The ad says“Hands-on experience with ROS 2, DDS, NVIDIA Jetson, and robot hardware interfaces”
The ad says“Hands-on experience with ROS 2, DDS, NVIDIA Jetson, and robot hardware interfaces”
The ad says“Experience optimizing models for real-time inference using ONNX, TensorRT, quantization, compilation, or similar tools”

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.

Close the gap between NVIDIA Isaac Sim and the physical robot. Our RAI team builds the core intelligence that powers REVEL robots, learning from the force and intent Neural Gambit captures.

Responsibilities

Worked directly with robotics physics simulators: Isaac Sim/IsaacLab, Newton, MuJoCo, or similar, at a level deep enough to reason about what the simulator is actually computing, not just how to call it
Comfortable in Python, and genuinely fluent in the physics underneath: how rigid bodies interact, how contact gets modeled, how actuators behave — this isn't a black box to you
Have calibrated a simulator against real hardware yourself, matched simulated behavior to measured behavior, not just eyeballed it
Have helped or deployed policies from simulation onto real robots, using techniques like domain or dynamics randomization, and know the ways that transfer tends to break

Requirements

3+ years of relevant professional experience
Experience integrating and deploying learned models or control policies on real robots
Strong Python and C++ skills for robot-facing and performance-critical code
Hands-on experience with ROS 2, DDS, NVIDIA Jetson, and robot hardware interfaces
Experience optimizing models for real-time inference using ONNX, TensorRT, quantization, compilation, or similar tools
Comfortable debugging timing, communication, inference, control, and hardware failures across the robotics stack, including latency, packet loss, and synchronization issues

Bonus qualifications

Experience connecting perception, learned policies, motion planning, and control into complete robotic systems
Experience deploying VLAs, Diffusion Policies, Transformers, or RL policies
Prior work with humanoids, legged robots, or highly dexterous robotic systems
Familiarity with sim-to-real transfer, control theory, real-time systems, or hardware-in-the-loop testing
Publications or strong open-source work in robotics, embodied AI, or ML systems

Why REVEL

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

What we offer

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

How to apply

Apply through the link on this posting.