Join an early-stage industrial automation team building computer vision systems to understand manual work in factories. You will work on the core perception system, helping bring real-time quality inspection from controlled experiments into production environments, with potential applications in robotic automation.
Build and improve vision pipelines for detection, classification, tracking, and temporal understanding.
Test first-person perception systems under camera motion, changing viewpoints, occlusion, motion blur, and variable lighting.
Use synthetic and real-world data to find failure cases and improve model performance.
Train, evaluate, and debug computer vision models using PyTorch.
Explore geometric vision, calibration, localization, pose estimation, and 3D perception.
Optimize models for low-latency inference and edge deployment.
Build evaluation tools and support deployments to learn how systems perform on factory floors.
Currently studying computer science, robotics, electrical engineering, mechanical engineering, or a related field.
Comfortable programming in Python, with hands-on experience in computer vision, machine learning, or robotics.
Hands-on experience with robotics or perception using real sensor or camera data.
Curiosity about computer vision and physical AI, plus the ability to learn quickly, build, debug, and take ownership.
Experience with PyTorch, OpenCV, object detection, tracking, synthetic data, SLAM/VIO, 3D vision, CUDA/TensorRT, ROS2, or NVIDIA Jetson is a plus.
Visa sponsorship is available. Relocation support from Germany is supported.
On-site in San Francisco, California, United States.