AI Insights
NVIDIA

Software Development Engineer in Test - SDET

NVIDIA · Santa Clara, California, US
full-timemid (3-7 yrs)Posted 21d ago
QA EngineeringIC2ICOn-siteVisa SponsoredRelocation
StackPythonShell ScriptingUnixLinuxTest AutomationAPI TestingUI TestingLLM BenchmarkingDeep Learning FrameworksCUDAOpenCLGPU HardwareTest Framework DesignBug Lifecycle ManagementCI/CD

Summary

NVIDIA is seeking an SDET to design, develop, and automate test frameworks for AI/ML software products, including LLM benchmarking and GPU-accelerated systems, collaborating with multi-functional teams at one of the world's leading semiconductor and AI computing companies.

About the role

NVIDIA is the world leader in GPU Computing. We are passionate about markets include gaming, automotive, professional vision, HPC, datacenters and networking in addition to our traditional OEM business. NVIDIA is also well positioned as the ‘AI Computing Company’, and NVIDIA GPUs are the brains powering modern Deep Learning software frameworks, accelerated analytics, modern data centers, and driving autonomous vehicles. We have some of the most experienced and dedicated people in the world working for us. If you are dedicated, forward-thinking, and if working with hard-working technical people across countries sounds exciting, this job is for you.

We are now looking for a Software QA Development Engineer; you will collaborate with multi-functional groups. SWQA Developer Engineer at NVIDIA is responsible for test planning, execution, and reporting, you will also write scripts to automate testing, design and develop tools for QA team, or develop integration tests for validation, so QA Engineer can improve productivity or optimize test plan. As a SWQA Developer, you must identify weak spots and constantly design better and creative test plans to break software and identify potential issues. You will have a huge impact on the quality of NVIDIA's products.

What you’ll be doing:

  • Review product requirements and develop test matrix.

  • Build test plan, design test case, execute and report test progress, bugs, and results to management.

  • Automate test cases and assist in the architecture, crafting and implementing of test frameworks.

  • Manage bug lifecycle and co-work with inter-groups to drive for solutions.

  • In-house repro and verify customer issues/fixes.

What we need to see:

  • BS or higher degree or equivalent experience in CS/EE/CE plus equivalent with 3+ years QA experience.

  • Proficient in Unix/Linux and shell/python programming skills.

  • Rich experience in test cases development, tests automation in API/UI and failure analysis.

  • Solid experience with AI development tools, including creating test cases, automating test cases, and ensuring comprehensive code coverage, among other related tasks

  • Good knowledge and hands-on experience in model testing and LLM benchmarking

  • Good QA sense including attention to detail, problem-solving, data analysis, quality standards knowledge, time management etc.

  • Excellent communicator, fluent written and verbal English.

  • Good teamwork with ability to work independently.

  • Passion to learn new hardcore technology.

Ways to stand out from the crowd:

  • Experience working with NVIDIA GPU hardware is a strong plus

  • Background in deep learning frameworks is a plus

  • Experience in parallel programming ideally CUDA/OpenCL is a plus

What you'll do

1Review product requirements and develop test matrix
2Build test plans, design test cases, execute tests, and report progress/bugs/results to management
3Automate test cases and assist in architecting, crafting, and implementing test frameworks
4Manage bug lifecycle and collaborate with cross-functional teams to drive solutions
5Reproduce and verify customer-reported issues and validate fixes

Requirements

3+ years of QA experience with proficiency in Unix/Linux and Python/shell scripting for test automation
Hands-on experience building test plans, automating test cases across API and UI layers, and conducting failure analysis
Solid experience with AI development tools including code coverage, test case creation, and automation for AI/ML workflows
Good knowledge of model testing and LLM benchmarking practices
Strong attention to detail, problem-solving, data analysis skills, and ability to communicate fluently in English

Nice to have

NVIDIA GPU Hardware
Deep Learning Frameworks
CUDA
OpenCL
Parallel Programming

Role overview

Role family
QA Engineering
Level
IC2 — qa_testing
Experience
3–7 years
Type
Individual Contributor
Remote policy
On-site
Visa sponsorship
Available

Tech stack analysis

LANGUAGES
PythonShell/Bash
FRAMEWORKS
PyTorch (preferred)TensorFlow (preferred)CUDAOpenCL
INFRASTRUCTURE
Unix/LinuxCI/CD pipelines
TOOLS
LLM benchmarking toolsBug tracking systems (e.g., Jira)Test management platforms

Salary estimate

$130K – $185K
AI-estimated salary range
Confidence75%
Reasoning

Salary not disclosed. Based on NVIDIA's known compensation bands for mid-level SDET roles in Santa Clara, CA, and comparable Levels.fyi/Glassdoor data for semiconductor/AI companies at this seniority (3+ years, BS/MS in CS/EE), total base salary typically ranges from $130K–$185K. NVIDIA is known to be a top-paying employer; RSUs and bonuses could add significantly on top.

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Green flags

4 items
NVIDIA is the dominant AI computing company — working here provides unmatched exposure to GPU, deep learning, and LLM technologies at the frontier.growth

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Benefits breakdown

HEALTH & WELLNESS
Typical NVIDIA benefits include comprehensive medical, dental, and vision coverage

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Hiring insights

JD quality
Urgency
medium
Autonomy
medium
Team size
medium (5-15)

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Red flags

PRO3 items
No salary range disclosed — candidates must negotiate blind, which can disadvantage applicants unfamiliar with NVIDIA's pay bands.compensation

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Interview insights

PRO
Rounds
5
Duration
4 wks
Difficulty
hard
Take-home
No

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Career path

PRO
Next roles
Senior SDETQA ArchitectML Infrastructure Engineer

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About the company

NVIDIA is the world's leading designer of GPUs and AI computing platforms. Its chips power everything from gaming and data centers to autonomous vehicles and scientific research. With a market cap exceeding $2 trillion, NVIDIA's CUDA platform and AI accelerators have become the backbone of the global AI revolution.

HQSanta Clara, CA, USA
Interview difficultyhard
Build vs Maintainboth
Cross-functionalYes