Reinforcement Learning Internhsip

Change the face of robotics with us!

Enchanted Tools / Changing the face of robotics

At Enchanted Tools, we believe a robot can be seriously useful and genuinely lovable. That's been our bet since 2021 — and it's starting to pay off.

We build the Mirokaï: character robots designed to work alongside teams in demanding professional environments, supporting teams in their daily work while bringing a touch of joy to the people around them. Our clients are primarily healthcare and care organizations in France and the United States.

Laureates of the French Tech 2030 program, Deeptech-certified by BPIfrance, we raised the largest seed round in the history of French robotics. Our robots have been showcased at CES Las Vegas and VivaTech — three years running. Our Urban Factory opened in Paris in late 2024.

We are now entering our industrialization and large-scale commercial phase, with a target of 100,000 robots produced over the next ten years. If you want to build something that didn't exist before, you're in the right place.

About the Role

As a Reinforcement Learning Intern, you will help bring perception into Mirokaï's navigation stack. You will integrate depth camera and time-of-flight sensor models into our RL environments, and use these rich perceptual inputs to train navigation policies that can handle real-world obstacles and spaces. This internship offers deep hands-on experience at the intersection of sensor simulation, reinforcement learning, and sim-to-real transfer.

What You'll Be Doing

• Integrate depth camera and time-of-flight sensor models into our Isaac Lab simulation environments.

• Design observation spaces and policy architectures that leverage perceptual inputs for obstacle-aware navigation.

• Train and evaluate reinforcement learning policies conditioned on simulated sensor data.

• Analyze navigation performance, robustness to sensor noise, and sim-to-real transfer aspects.

• Integrate trained policies into the Mirokaï software stack and validate them on physical robots.

• Stay up to date with recent research in perceptive RL and learning-based robot navigation.

Profile

• Bac+5 in Robotics, engineering, computer science, or related field.

• Coursework or project experience in reinforcement learning or learning-based control.

• Strong Python skills and knowledge of a deep learning framework (PyTorch, TensorFlow, etc).

• Familiarity with simulation environments such as Isaac Sim, Mujoco, or Gazebo is a plus.

• Solid analytical and problem-solving abilities.

Bonus Points For

• Experience implementing RL algorithms.

• Familiarity with robot control, dynamics modeling, or motor control.

• Prior work in sim-to-real transfer or domain randomization.

These interviews can be conducted entirely via video call, but we warmly invite you to visit your potential future colleagues, the Mirokaï, in our Paris office!

Don’t wait any longer — join us!