Lead design and implementation of ML tools for scenario classification, anomaly detection, sensor data quality, behavior analytics, and automated reporting. Build scalable pipelines and automated data curation for camera, radar, and LiDAR; create SIL/HIL replay interfaces, ground-truthing and annotation tools, and offline benchmarking. Integrate ML modules into simulation, HIL/SIL, and CI/CD ecosystems, implement reproducible workflows (Docker, CI/CD, artifact versioning, automated tests), monitor model/data quality and mentor junior engineers while collaborating with verification and perception teams.

Design, implement, and optimize high-performance embedded inference systems for automotive ADAS. Develop and integrate ML runtimes on edge SoCs, ensure low-latency concurrency and I/O, integrate with ROS2/sensor frameworks, deploy to SIL/HIL and CI pipelines, and perform profiling and debugging of perception stacks (camera/radar/LiDAR) under real-time constraints.