Senior ADAS Perception Engineer - Deep Learning & Embedded

Requirements 3–5+ 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 , (Desirable) Comprehensive understanding of machine learning algorithms, including classification, regression, and clustering methods , (Desirable) Experience deploying and optimizing models for embedded or automotive SoCs (e.g., NVIDIA Drive, TI TDA4, Qualcomm Snapdragon) , (Desirable) Proficiency in model optimization techniques such as quantization, pruning, and knowledge distillation , (Desirable) Doctorate (Ph.D.) in Computer Science, Artificial Intelligence, or related field is a plus , (Desirable) Strong programming experience in Python and/or C++ within Linux development environments , (Desirable) Familiarity with automotive perception workflows, datasets, and evaluation frameworks (e.g., KITTI, Waymo, Euro NCAP) What the job involves We are seeking a highly skilled Senior Deep Learning Engineer to drive the development and deployment of advanced perception models for Advanced Driver Assistance Systems (ADAS) , The successful candidate will play a key role in designing cutting-edge neural network architectures, optimizing model performance, and ensuring reliable deployment on embedded platforms , This position requires a balance of deep technical expertise, strong analytical thinking, and cross-functional collaboration , 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 #J-18808-Ljbffr