Applied Computer Vision Engineer - Data Driven Development

About the role

The Video Perception (VIPer) team at Bosch is building perception systems for L2+ Advanced Driver Assistance Systems. As an Applied Computer Vision Engineer you will work on the full development lifecycle, from data analysis to deploying deep‑learning models in real‑time embedded environments.

Key responsibilities

• Develop, train and validate deep‑learning models for object detection, semantic segmentation and lane detection.
• Design and run experiments, analyse performance metrics and identify failure modes.
• Process large‑scale unstructured video data to create high‑quality training datasets.
• Build and maintain robust MLOps pipelines for data handling, model training and deployment.
• Implement and optimise Python algorithms to meet real‑time constraints on embedded hardware.
• Document and present experimental results, architectural decisions and technical findings to stakeholders.

Required profile

• Bachelor’s degree in Computer Science, Electrical Engineering or a related field.
• 3‑8 years of professional experience in computer vision or machine‑learning application development.
• Strong programming skills in Python and solid object‑oriented design knowledge.
• Hands‑on experience with modern deep‑learning frameworks such as PyTorch or TensorFlow 2+.
• Deep understanding of CNNs, object detection, segmentation and interest in vision Transformers.
• Proficiency with development tools including Git, Docker and a Linux environment.

Required skills

• Python
• PyTorch
• TensorFlow
• Convolutional Neural Networks (CNN)
• Object detection
• Semantic segmentation
• Lane detection
• Vision Transformers
• Git
• Docker
• Linux
• MLOps pipelines
• Model optimisation (e.g., TensorRT)