AI Engineer
Vor 7 Tagen
- Develop software-engineering solutions for the integration of machine-learning models in robotics environments
- Develop intelligent perception and control models using VLMs, transformers, CNNs, RNNs, and RL to power robotic behaviors.
- Fine-tune and adapt modern pre-trained models for robotic vision, policy learning, etc.
- Use large-scale pre-trained networks to develop robust, generalizable ML pipelines.
- Integrate ML with real-time control, combining traditional robotics methods with learned components.
- Use simulation environments like Isaac Sim and MuJoCo to prototype and evaluate models prior to real-world deployment.
- Close the sim-to-real gap
- Collaborate cross-functionally with controls, perception, systems, and hardware teams to bring ML-driven robotics to life.
- Write high-quality, scalable code in Python using ML frameworks like PyTorch and JAX; contribute to shared tooling and research infrastructure.
- Very strong software-engineering background in robotics
- Very strong experience using ROS 2
- Strong background in machine learning for vision and control.
- Deep experience with Python, PyTorch, and/or JAX; comfortable debugging, optimizing, and scaling ML systems.
- Experience with simulators like Isaac Sim or MuJoCo for training and testing robotics pipelines.
- Comfort integrating ML into control systems, including hybrid and end-to-end approaches.
- MSc or PhD in Machine Learning, Robotics, Computer Science, or related field—or equivalent practical experience
- Willingness to travel to customer sites as well as to US HQ.
- Prior experience deploying pre-trained models in embedded or edge environments.
- Familiarity with reinforcement learning in robotics applications.
- Interest in active perception, multi-agent robotics, or HRI (human–robot interaction).
- Contributions to open-source robotics and publications.
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