Motherson Innovations Tech Limited

What We offer: At our headquarters in Noida, you will find an innovative international automotive environment that is at the same time familiar and appreciative. Exciting tasks with interesting and secure prospects in a global company Attractive salary Very good professional and personal development Work with a cutting-edge team in a rapidly growing field. Collaborative environment with world-class experts in AI, imaging, and embedded systems. Role & responsibilities: Lead the design, development, and optimization of AI/ML algorithms for ADAS functionalities such as object detection, lane detection, pedestrian recognition, and path planning. Develop multi-sensor fusion frameworks involving cameras, radars, LIDAR, and IMUs for perception and localization. Drive the deployment and inference optimization of ML models on edge computing platforms (e.g., NVIDIA Jetson, Qualcomm, TI, or custom SoCs). Mentor and guide a team of algorithm engineers and researchers to meet technical milestones. Collaborate with cross-functional teams (hardware, systems, validation, cloud) to ensure system-level performance and safety. Ensure alignment with ASPICE, ISO 26262, and other automotive standards. Conduct regular code and architecture Preferred candidate profile: 6-10 years of experience in AI/ML algorithm development, with at least 5 years in the ADAS domain. Master's degree or higher in Computer Science, Electrical Engineering, or a related field. Ph.D. is a plus. Strong background in automotive (ADAS, DMS, object detection) Experience in leading algorithm integration into production ADAS ECU/software platforms. Excellent communication, leadership, and mentoring abilities. Technical Skills : Programming: Python, C++, TensorFlow, PyTorch, OpenCV. Algorithms: Image enhancement, super-resolution, denoising, segmentation, feature extraction. ML Techniques: CNNs, GANs, transformers for vision, classical computer vision. Domain Knowledge: Automotive standards (ISO 26262, ASPICE), DICOM, HL7, medical imaging formats. Optimization: Quantization, pruning, real-time inference on GPUs, NPUs, embedded systems. Tools: Docker, Kubernetes, CI/CD, cloud platforms (AWS, Azure, GCP). Data Handling: Large-scale image datasets, annotation workflows, synthetic data generation. Deployment: Edge devices (NVIDIA Jetson, Qualcomm, TI)