3 days ago
Shanghai, China +2 moreSenior
Responsibilities
- Design, curate, and maintain test suites covering system requirements, edge cases, failure modes, road types, environmental conditions, and vehicle interactions.
- Architect and implement scalable evaluation tools and metrics for system testing, verification, and release decisions.
- Build ground-truth datasets and LLM- and VLM-powered auto-labeling solutions for ADAS data workflows.
- Develop simulation, replay testing, benchmarking, and CI/CD infrastructure for repeatable large-scale validation.
- Create labeling guidelines, standard operating procedures, and quality-control mechanisms for internal and external labeling teams.
- Identify, reproduce, debug, and resolve system anomalies in collaboration with cross-functional engineering teams.
- Execute offline validation, analyze complex datasets, and produce actionable reports for system improvements.
- Ensure validation activities follow automotive safety standards, regulatory requirements, and industry best practices.
Requirements
- Master's degree or Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related technical field, or equivalent practical experience.
- At least 6 years of hands-on experience in software development, testing, validation, or quality engineering.
- Hands-on experience with ADAS or autonomous driving systems, including simulation, replay-based testing, and model-based development workflows.
- Strong programming and scripting skills in Python, C++, and Shell.
- Fluency with unit, integration, system, and end-to-end testing practices.
- Experience designing or maintaining automated testing, benchmarking, or CI/CD infrastructure.
- Familiarity with LLMs, VLMs, and AI agents applied to workflow automation, data labeling, or engineering productivity.
- Strong analytical, debugging, problem-solving, and cross-disciplinary collaboration skills.
- Preferred experience includes large-scale data analysis, advanced SQL, production ADAS validation, LLM/VLM/agentic development frameworks, distributed data-processing systems, and large-scale simulation infrastructure.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.