Technical Lead

1 week ago

Noida, Uttar Pradesh, India Motherson Innovations Tech Limited Full time ₹ 20,00,000 - ₹ 25,00,000 per year

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)

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