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# Senior Machine Learning Engineer Computer Vision for Robotics

- **Company:** Agile Robots Ag
- **Location:** München, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Computer Vision, C++ (Programming Language), Nvidia CUDA, Computer Programming, Middleware, Python (Programming Language), Kinematics, Machine Learning, Software Engineering, Reinforcement Learning, Pytorch, Deep Learning, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Lidar
- **Published:** October 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c57b94ebf0fc88a8

## About the Role

* Background: Master's degree or PhD in computer science, robotics, electrical engineering, or a related field, or an equivalent combination of research and industry experience, with three or more years in machine learning, computer vision, or robotics.
 * ML Pipeline: Hands-on experience designing, training, and tuning deep-learning models end to end in PyTorch.
 * Method Development: Experience adapting, combining, or extending existing machine learning or vision methods for a new task, with changes that required understanding how those methods work internally.
 * Computer Vision: Solid understanding of computer vision and 2D/3D geometry.
 * Programming: Software development in Python, with working knowledge of C++., * Robot Control: Understanding of robot kinematics, feedback control, or visual servoing, or experience with high-precision manipulation. The team supports candidates in building this experience on the job.
 * Model Deployment: Experience deploying optimized models with ONNX, TensorRT, or comparable frameworks, including on embedded or industrial GPU platforms.
 * Robot Learning: Familiarity with reinforcement learning or imitation learning.
 * Industrial Cameras: Experience with industrial 2D/3D cameras, lens models, camera calibration, or point clouds.
 * Robotics Middleware: Experience with ROS or ROS 2.

## Description

* Method Development: Develop deep-learning methods for visual servoing and robotic vision, adapting and combining existing methods for new tasks where that requires significant changes.
 * Model Training: Design, train, fine-tune, and evaluate models in PyTorch, including adapting pretrained and foundation models to real robot tasks.
 * Perception Methods: Develop 2D and 3D perception for pose estimation, depth, tracking, and geometric understanding, combining learned, classical, and optimization-based methods where each fits.
 * Closed-Loop Integration: Integrate learned models into closed-loop robot control for high-precision manipulation.
 * Robot Evaluation: Evaluate models on real robots and precision manipulation tasks, analyse failure cases, and improve robustness and generalization.
 * Real-Time Deployment: Build real-time inference pipelines in C++ and Python and optimize models with ONNX, TensorRT, and CUDA to meet the timing of the control loop.