Process Scaling: Create and refine standard operating procedures, labeling guidelines, and quality-control mechanisms, enabling established workflows to scale across internal and external labeling teams.
Issue Triage and Resolution: Identify, reproduce, and debug system anomalies. Collaborate closely with cross-functional teams to determine root causes, drive timely resolution, and validate software fixes.
Test Execution and Analytics: Execute offline validation using internal test frameworks, analyze large and complex datasets, and produce reports with actionable insights that guide system improvements.
Safety and Compliance: Ensure validation activities follow applicable automotive safety standards, regulatory requirements, and industry best practices.
What we need to see
Master's degree or Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related technical field, or equivalent practical experience.
6+ years of hands-on experience in software development, testing, validation, or quality engineering, ideally in autonomous driving, automotive systems, robotics, or another safety-critical domain.
Hands-on background in ADAS or autonomous driving systems, covering simulation, replay-based testing, and model-based development workflows.
Strong programming and scripting skills in Python, C++, and Shell.
Fluency with modern software testing practices, including unit, integration, system, and end-to-end testing.
Experience designing or maintaining automated testing, benchmarking, or CI/CD infrastructure.
Familiarity with modern AI technologies—including LLMs, VLMs, and AI agents—and a track record applying them to workflow automation, data labeling, or engineering productivity.
Strong analytical, debugging, and problem-solving skills, with the ability to work effectively across multiple engineering disciplines.
Ways to stand out from the crowd
A strong automation mindset and a track record of building software, platforms, or internal tools that significantly improve engineering efficiency and scalability.
Hands-on work with data science methodologies, large-scale data analysis, and advanced SQL.
Direct experience developing or validating production ADAS or autonomous driving features.
Depth in LLM/VLM/agentic development frameworks.
Exposure to distributed data-processing systems, large-scale simulation infrastructure, or agentic AI workflows.
NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. We do not discriminate in our hiring or employment practices on the basis of race, religion, color, national origin, gender, gender identity or expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.
We are home to some of the forward-thinking and dedicated people in the world. If you are creative, self-motivated, and passionate about building reliable systems at scale, we would love to hear from you.