Clera
Robot Learning Engineer
About this role
Join an early-stage industrial robotics startup in Munich as a Robot Learning Engineer to develop and deploy machine learning solutions for autonomous manufacturing tasks like surface finishing and welding. You'll work directly with physical robotic systems, applying computer vision, reinforcement learning, and imitation learning to solve real-world automation challenges from day one.
What you'll do
- Research and evaluate ML models for robot perception and task understanding on industrial hardware
- Apply computer vision and deep learning techniques to multi-modal sensor data including cameras and force/torque sensors
- Develop and experiment with reinforcement learning and imitation learning approaches for robot control
- Integrate trained AI models into the ROS 2 robotics middleware stack
- Design and execute rigorous experiments with rapid iteration cycles on real robotic work cells
- Bridge research prototypes with factory deployment requirements and manufacturing constraints
What they're looking for
- Python programming
- Deep learning frameworks (PyTorch or TensorFlow)
- ROS 2 robotics middleware
- Computer vision and image processing
- Reinforcement learning
- Imitation learning
- Sensor data processing and fusion
- Sim-to-real transfer techniques
Benefits
- Equity participation in early-stage startup
- Competitive cash compensation for startup stage
- Hands-on experience deploying ML to physical factory systems
- Work on cutting-edge industrial automation and robotics challenges
- Fully on-site collaborative environment in Munich
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Clera
Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.
View all jobs at CleraLikely interview questions
- Walk us through a robotics or ML project where you went from simulation or prototype to real-world deployment—what were the key challenges?
- Describe your experience with ROS 2: what have you built, and how comfortable are you integrating deep learning models into a ROS pipeline?