Level

NVIDIA

Senior Software Engineer, Test Engineering

NVIDIA is hiring a Senior Software Engineer, Test Engineering in Beijing, China. Level rates it ; you can apply on Level.

AI in this role

Build and scale simulation and validation pipelines, auto-labeling solutions, and test automation for ADAS.

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ai-agentsai-data-labelingtest-engineeringsimulationdata-annotationadasautomation

NVIDIA is at the forefront of groundbreaking advances in artificial intelligence, high-performance computing, and simulation. The company is widely recognized as one of the technology industry's most desirable employers. We are looking for a Senior Software Engineer to join the Test Engineering team for NVIDIA Driver Assistance Systems (NDAS). In this role, you will help build and scale simulation and validation pipelines for advanced driver assistance systems. Your work will span data mining, automated labeling, benchmarking, CI/CD infrastructure, AI-assisted workflows, and large-scale test automation. Close collaboration with machine learning engineers, software developers, and systems engineers shapes the tools, datasets, metrics, and processes needed to evaluate complex autonomous driving systems.


What you'll be doing:

  • Test Case Engineering: Design, curate, and maintain comprehensive test suites covering system requirements, edge cases, and failure modes across diverse road types, environmental conditions, and vehicle interactions. 
  • Tooling and Metrics Development: Architect and implement scalable evaluation tools and metrics that support critical system testing, verification, and release decisions. 
  • AI-Assisted Data Pipelines: Build ground-truth datasets for ADAS features and develop LLM- and VLM-powered auto-labeling solutions that accelerate and scale data annotation workflows. 
  • Simulation and Validation Infrastructure: Develop and improve simulation, replay testing, benchmarking, and CI/CD pipelines for efficient, repeatable, and large-scale validation. 
  • 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.

How we rate this

Senior Software Engineer, Test Engineering at NVIDIA rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsAI Data LabelingTest EngineeringSimulationData AnnotationAdasAutomationLLMs

Also common in software engineer jobs at this level

Pay

Typical software engineer job
$168k

This posting doesn't list pay. Use the typical software engineer figure when pay comes up. See software engineer jobs

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. How do you keep labeling instructions consistent across a large annotation team?
  3. Tell me about a project where test engineering was part of your work. What did you do?
  4. Tell me about a project where simulation was part of your work. What did you do?
  5. Tell me about a project where data annotation was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI agents, AI Data Labeling, Test Engineering, Simulation, and Data Annotation. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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