Sr. Computer Vision Engineer (Deep Learning)

Responsibilities

• Design and implement advanced deep learning architectures to enhance perception capabilities within ADAS systems.
• Maintain and continuously improve existing models by optimizing performance, addressing issues, and refining architecture and algorithms.
• Perform detailed root cause analysis of production issues and develop sustainable, high-quality solutions.
• Optimize model performance with a focus on latency, efficiency, and resource utilization for real-time embedded deployment.
• Integrate and validate deep learning algorithms on automotive-grade hardware and embedded SoCs.
• Collaborate closely with data engineering, data annotation, and platform engineering teams to ensure smooth data flow and seamless model integration.
• Provide regular updates and technical reports on model development, maintenance progress, and performance metrics to management.
• Required Qualifications
• 5+ years of professional experience developing, training, validating, and deploying deep learning-based perception models for ADAS or related computer vision applications.
• In-depth understanding of training and inference pipelines, including data loading, augmentation, and loss function design.
• Advanced degree (M.S. or Ph.D.) in Computer Vision, Robotics, Machine Learning, or a closely related discipline, or equivalent industry experience.
• Strong proficiency in Python and a deep understanding of software design principles and development best practices.
• Expertise in PyTorch (preferred) or TensorFlow for large-scale model development and experimentation.
• Practical experience with data pipelines, distributed training, and machine learning experiment management tools.
• Proven ability to work effectively in a collaborative, cross-functional team environment.
• Preferred Qualifications
• Comprehensive understanding of machine learning algorithms, including classification, regression, and clustering methods.
• Experience deploying and optimizing models for embedded or automotive SoCs (e.g., NVIDIA Drive, TI TDA4, Qualcomm Snapdragon).