About the role
Senior Embedded AI Engineer (Automotive / ADAS) Job Overview We are seeking a highly skilled Embedded AI Engineer to design and optimize next-generation artificial intelligence perception systems for Automotive ADAS/AD applications. In this role, you will bridge the gap between cutting-edge deep learning models and resource-constrained embedded hardware. You will support the evaluation of AI Systems-on-Chip (SoCs), ensuring our perception algorithms run efficiently, reliably, and at peak performance on the edge. Roles and
Responsibilities Develop AI Perception Systems: Design, implement, and deploy artificial intelligence-focused perception pipelines for automotive systems. Optimize Edge Deployment: Deploy deep learning models on edge AI SoCs and optimize inferencing applications on accelerators like DSPs, NPUs, TPUs, or GPUs. Integrate Algorithms: Define clear software interfaces, implement efficient code, integrate algorithms, and lead testing activities. Prototyping & Benchmarking: Build functional prototypes for the proof-of-concept phase and benchmark algorithm performance. Hardware Selection: Evaluate hardware options and collaborate as part of the Subject Matter Expert (SME) team for automotive AI SoC selection. Cross-Functional Collaboration: Act as a technical bridge, communicating seamlessly with interdisciplinary algorithm experts and system developers. Your Background B.Tech, Master’s, or Ph.D. in Computer Science, Automotive Engineering, Robotics, or a related field. Experience & Core Skills Overall Experience: 6+ years of professional software engineering experience. Embedded Software: Minimum of 2 years of proven, hands-on experience in embedded software development. Programming Mastery: Strong programming skills in C and C++, including the expert application of compiler and debugging methods. Software Optimization: Deep understanding of code optimization approaches and memory dependencies. Analytical Mindset: Good analytical skills, a strong automotive mathematical background, and general knowledge of perception and machine learning algorithms. Edge AI & Hardware Expertise (Must-Haves) Compiler & Hardware Knowledge: Good knowledge of AI/ML compilers, instruction sets, and memory architectures (FPGA, DSP, and AI processors). Model Compression: Practical experience with model compression techniques, including model pruning, quantization, fixed-point arithmetic, and mixed precision.
