Bosch Chassis Systems India Private Ltd. is hiring an Applied Computer Vision Engineer - Data Driven Development

The Video Perception (VIPer) team develops perception systems for L2+ Advanced Driver Assistance Systems (ADAS). As an Applied Computer Vision Engineer, you will be involved in the full development lifecycle of the perception stack. You will work on real-world problems by analyzing large datasets, experimenting with Deep Learning models like Convolutional Neural Networks (CNNs) and Transformers, and contributing to production-ready software. Responsibilities •Develop, train, and validate deep learning models for perception tasks such as object detection, semantic segmentation, and lane detection. •Contribute to the design and implementation of experiments, performing rigorous analysis of model performance and identifying failure modes. •Analyze large-scale, unstructured video data to derive insights and assist in curating high-quality datasets for model training. •Collaborate with the team to build and maintain robust MLOps pipelines for data processing, training, and deployment. •Implement and optimize algorithms in Python, ensuring they meet the performance requirements for real-time embedded systems. •Document and present experimental results, architectural choices, and technical findings to the team and stakeholders. Required Qualifications •Education: Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. •Experience: 3-8 years of professional experience in computer vision or machine learning application development. •Programming: Proficiency in Python and a strong understanding of object-oriented programming. •Deep Learning Frameworks: Strong hands-on experience with modern DL frameworks such as PyTorch or TensorFlow 2+. •Core Concepts: Solid understanding of deep learning fundamentals, including CNNs, object detection, and segmentation. A keen interest in learning and applying Transformers for vision is essential. •Tools: Familiarity with essential software development tools like Git, Docker, and working in a Linux environment.