Compiler Verification Engineer, Compute Performance – GPU

Posted Yesterday
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2 Locations
In-Office
140K-224K Annually
Mid level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop and automate compiler verification tests for GPU compute performance. Analyze functional defects and performance regressions, identify root causes, track issues to resolution, and collaborate with compiler developers. Create test plans, implement and integrate test cases, maintain performance baselines, generate reports and statistics, investigate outliers, and improve testing processes. Work includes programming, build and test automation, execution, reporting, and performance trend analysis.
Summary Generated by Built In

NVIDIA's invention of the GPU 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, we are increasingly known as “the AI computing company”.

We are looking for a Verification Engineer, Compute Performance to join our Compiler Verification Team.

What you’ll be doing:

  • Analysis: Analyze performance degradation or functional defect of compilers, identify regression root cause, suggest corrective action, and perform reviews to continuously improve testing.

  • Test Automation: Automate compiler testing using NVIDIA test frameworks and by programming. Includes test execution, test reporting, and results analysis and automation of build and test environments. Work with software compiler developers and assist in providing automated solutions for unit testing.

  • Test Operations: Utilize test suites to find, report and track compiler performance changes. Work with development team to drive regressions to resolution. Generate statistics based on performance data, identify and investigate outliers and monitor performance trends. Maintain historical data and baselines for comparison.

  • Compiler Test Development: Develop and review test plans, implement test cases, automate tests, integrate tests into NVIDIA test management frameworks, port 3rd party testing, and author test reports. May include integrating already existing tests into the compiler test automation.

  • Process Improvement: Utilize current iterative planning and test development processes. As part of team, identify potential or observed weaknesses in current process, offer ideas for actions that can improve quality, and participate in quality initiatives.

What we need to see:

  • Bachelor’s or Master’s Degree or equivalent experience.

  • 3+ years’ work experience in a software development or test organization

  • Excellent communications skills, self-motivated and well organized.

  • Deep understanding of Software Development Life Cycle (SDLC), High-Performance Computing (HPC), and Software Testing Methodologies.

  • Compiler Domain Expertise: You should understand how compilers work and how compilers are implemented. Proven strength in problem solving and implement solutions.

  • Ability to work with various teams to generate a solution for performance regression and be productive under tight schedules, and have strong analytical skills with attention to detail.

  • Be able to apply existing skills to new situations. Break large problems into smaller problems and further triage difficult performance regressions.

  • You have experience writing test plans, test development, test automation, test execution and reporting in a production environment.

  • Programming Languages: Have experience programming and/or testing in C/C++/CUDA as well as scripting languages (Python, Perl, Shell)

Ways to stand out from the crowd:

  • Proficient industry experience in testing production software. Preferably compiler or other system software.

  • Previous compiler development and/or compiler verification/test or performance analysis experience.

  • Experience with NVIDIA CUDA Toolkit, especially solving issues and debugging in Linux environment.

  • Background with revision control software and management tools, such as Git, Perforce, JIRA, Confluence and Make.

  • Familiarity with statistical analysis tools for identifying and isolating out-of-bound behavior

With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most brilliant and hardworking people in the world working with us and our product lines are growing fast in some of the hottest state of the art fields such as Virtual Reality, Artificial Intelligence, Deep Learning and Autonomous Vehicles.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 140,000 USD - 224,250 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 4, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Skills Required

  • Bachelor's or Master's degree, or equivalent experience
  • 3+ years of work experience in a software development or test organization
  • Excellent communication skills; self-motivated and well organized
  • Deep understanding of software development life cycle, high-performance computing, and software testing methodologies
  • Understanding of compiler operation and implementation
  • Strong problem-solving, analytical, triage, and attention-to-detail skills
  • Experience writing test plans, developing tests, automating tests, executing tests, and reporting results in a production environment
  • Programming or testing experience with C, C++, CUDA, and scripting languages such as Python, Perl, or Shell
  • Industry experience testing production software, preferably compilers or system software
  • Previous compiler development, compiler verification, compiler testing, or performance analysis experience
  • Experience with the NVIDIA CUDA Toolkit and debugging in a Linux environment
  • Experience with revision control and management tools such as Git, Perforce, JIRA, Confluence, and Make
  • Familiarity with statistical analysis tools for identifying and isolating out-of-bound behavior

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.

NVIDIA Insights

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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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