Intern (m/f/d) Robot Learning

The AI Teams at Agile Robots are looking for an Intern (m/f/d) Robot Learning, who will help train and evaluate machine-learning models for real robots during a six-month student internship.

This posting is for our intern talent pool and serves as an entry point to several of our AI teams. We review applications on a rolling basis and contact you when an internship matching your profile becomes available, typically several months before its start date. We match you with a team and a project during the interview process, and your exact responsibilities depend on that match.

Possible Responsibilities

• Agentic Planning: Build and test systems that use language or vision-language models to plan, sequence, and monitor robot tasks over multiple steps.
• Sim-to-Real Transfer: Apply domain randomization and realism modeling to reduce the gap between simulated and real sensor and actuator data.
• Model Training: Train, fine-tune, or adapt machine-learning models for robot perception, manipulation, or task planning, including adapting an existing model to a specific robot and task.
• Model Evaluation: Design and run experiments in simulation and on real robots to measure where a model works and where it fails, and trace failures to the data, the model, or the setup.
• Data Preparation: Collect, clean, and annotate robot data, including teleoperation demonstrations and experiment records from real hardware.

Essential Skills

• Degree Programme: Enrolled in a Master’s programme in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field.
• Machine Learning: Hands-on experience training and evaluating neural networks in PyTorch or a comparable framework, gained through coursework, projects, or research.
• Project Experience: At least one machine-learning project in which you prepared the data and designed the evaluation yourself.
• Python Programming: Working proficiency in Python for machine-learning and robotics development.
• Robot Simulation: Working familiarity with at least one physics simulator, such as MuJoCo, Isaac Sim, or Gazebo.

Beneficial Skills

• Language Agents: Experience building a system in which a language or vision-language model plans or triggers actions in a simulator or on a robot.
• Robot Learning: Experience with imitation learning, reinforcement learning, or vision-language-action models.
• Sensor Modeling: Exposure to sensor noise models, actuator dynamics, or domain randomization techniques.
• Robot Hardware: Experience running code on a physical robot or sensor setup, for example in a university lab or a student robotics team.
• ROS 2: Familiarity with ROS 2 and how robot software components communicate.