We are looking for a Lead AI Engineer with deep expertise in Computer Vision and Edge AI to lead the development and deployment of perception systems in next-generation automotive platforms. This role focuses on building high-performance AI models for Vehicles like ADAS, in-cabin monitoring etc and deploying them efficiently on embedded SoC platforms. You will work on the full AI pipeline from data to deployment ensuring models meet real-time performance and safety requirements.

Job description Key Responsibilities:

Design, train, and optimize computer vision models for real-time applications like object detection, lane tracking, semantic segmentation, and driver state monitoring.
Port and deploy AI models to various automotive SoC platforms (e.g., NVIDIA, Qualcomm, NXP, TI), optimizing for performance, memory, and power efficiency.
Use tools like TensorRT, ONNX, TVM, and OpenVINO to quantize, compress, and accelerate models for edge inference.
Work with embedded systems teams to integrate AI modules into in-vehicle software stacks.
Collaborate with perception, sensor fusion, and hardware teams to deliver robust and reliable end-to-end systems.
Evaluate models on large, multi-sensor datasets and conduct rigorous performance and safety testing under varied driving conditions.
Stay current with the latest developments in edge AI, model optimization, and automotive perception.
Provide technical mentorship and code reviews for junior engineers and researchers.

Required Qualifications:

Bachelor s or Master s degree in Computer Science, Electrical Engineering, Robotics, or a related field
6 8 years of experience in AI/ML, with at least 4 years in computer vision and model deployment in real-world systems.
Proven experience deploying deep learning models on SoC/embedded hardware platforms (NVIDIA Jetson/Drive, Qualcomm Snapdragon, TI TDA4, etc.).
Expertise in Python and C++; strong hands-on knowledge of PyTorch, TensorFlow, and OpenCV.
Deep understanding of model optimization techniques (e.g., quantization, pruning, INT8/FP16 inference).
Familiarity with tools such as TensorRT, ONNX, TFLite, and performance profilers for embedded AI.
Strong foundation in real-time system constraints, embedded development, and edge compute limitations.

Prefered Qualifications:

Experience with ADAS, OMS, DMS
Knowledge of automotive-grade software development and safety standards.
Background in working with CAN, Ethernet, or other in-vehicle communication protocols.

What We Offer

The opportunity to lead the development of cutting-edge vision and AI systems powering intelligent vehicles.
Work on real-world deployment challenges with top-tier automotive partners.
Competitive compensation, flexible work options, and access to modern AI/edge infrastructure.
A culture that encourages innovation, technical excellence, and continuous learning.

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