Senior Software Engineer, Test Engineering

Posted 4 Days Ago
Be an Early Applicant
3 Locations
In-Office
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop and scale simulation, validation, benchmarking, and CI/CD pipelines for NVIDIA driver assistance systems. Design comprehensive test suites, evaluation tools, metrics, AI-assisted labeling workflows, and automated testing infrastructure. Analyze large datasets, debug system anomalies, validate fixes, and ensure compliance with automotive safety standards. Collaborate with machine learning, software, and systems engineers to improve autonomous-driving system reliability and engineering scalability.
Summary Generated by Built In

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.

Skills Required

  • 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
  • Experience in autonomous driving, automotive systems, robotics, or another safety-critical domain
  • Hands-on background in 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, with experience applying them to workflow automation, data labeling, or engineering productivity
  • Strong analytical, debugging, and problem-solving skills, with cross-functional collaboration ability
  • Experience building software, platforms, or internal tools that improve engineering efficiency and scalability
  • Experience 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, or agentic development frameworks
  • Exposure to distributed data-processing systems, large-scale simulation infrastructure, or agentic AI workflows

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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