Applied Computer Vision Engineer - Data Driven Development

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.
• 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.
• -Master's degree in a relevant field.
• -Prior experience in the ADAS, autonomous driving, or robotics domains.
• -Experience with model optimization and deployment (e.g., TensorRT, ONNX).
• -Familiarity with cloud platforms (AWS/Azure) and data processing tools (e.g., Spark).
• -Exposure to Large Language Models (LLMs) and agentic AI concepts.
• -While not required, hands-on experience with C++ is beneficial.

Benefits

Additional Information

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.

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