NVIDIA is well positioned as the 'AI Computing Company', our GPUs being the brains that power modern Deep Learning software frameworks, accelerated analytics, modern data centers, and driving autonomous vehicles. We are looking for a Senior Software QA Test Development Engineer to join in the mission of crafting a distributed technology for all NVIDIA teams that remotely manage 10s of 1000s of resources in a simple and controlled fashion, allowing engineers to focus on engineering and automation, rather than being burdened by manual operational tasks.
SWQA test developer engineers at NVIDIA are responsible for creating test plans, execution, and reporting, as well as developing scripts for test automation, designing and developing tools for the QA team, and developing integration tests for validation. As a test developer, you must identify weak spots and constantly design better and more creative test plans to break software and identify potential issues. You will have a huge impact on the quality of NVIDIA's products. The ideal candidate must have strong programming skills and hands-on experience using AI development tools to improve quality and productivity across the end-to-end QA workflow. This includes leveraging AI assistants for test automation, code generation, debugging, and enhancing testing efficiency. During the interview process, we will assess your ability to effectively use AI development tools and evaluate your programming capabilities to ensure you can deliver high-quality solutions.
What you’ll be doing:
Architect, implement, and evolve scalable agentic end to end SWQA workflow, automated test frameworks, infrastructure, and tooling for complex software products.
Define test strategy and quality gates across functional, integration, regression, reliability, and release-validation workflows.
Build and maintain high-value automated coverage for Linux-based, containerized, and Kubernetes environments.
Investigate difficult failures across software stacks; isolate root causes and work directly with development teams through resolution with proposed bug fix PRs.
Establish meaningful quality metrics, improve signal-to-noise in test results, and use data to prioritize engineering investment.
Drive automation efficiency through well-engineered tooling, reusable frameworks, and AI-assisted workflows where they demonstrably improve quality or developer productivity.
Raise the technical capability of the wider team through code reviews, design reviews, documentation, and mentorship.
Take ownership of ambiguous, cross-functional quality problems and deliver durable solutions—not just test execution.
What we need to see:
BS or higher degree or equivalent experience in Computer Science, Electronics or related discipline with 5+ years QA experience.
Significant hands-on experience in software QA, SDET, or test-development roles, with a demonstrated record of delivering production-quality automation.
Strong programming skills in at least one modern language used for test engineering, plus the ability to write clean, maintainable, and well-tested code.
Deep practical experience designing agentic end to end SWQA workflow, automation frameworks—not merely extending existing test scripts.
Strong Linux expertise and proven experience testing software deployed with Docker and Kubernetes.
Solid understanding of software quality methodology, including risk-based testing, testability, observability, regression strategy, and release criteria.
Ability to debug multi-layer failures spanning application code, services, infrastructure, networking, and deployment configuration.
Evidence of technical ownership: defining a problem, aligning stakeholders, delivering a solution, and measuring the outcome.
Clear written and verbal communication skills, with the ability to influence engineers and stakeholders through technical rigor.
Ways to stand out from the crowd:
Created or materially transformed a test platform used by multiple teams or products.
Reduced release risk, escaped defects, test runtime, or manual validation effort through measurable engineering improvements.
Demonstrated ownership of an agentic end-to-end SWQA workflow.
Experience validating cloud-native, distributed, or high-scale systems under realistic failure conditions.
Recognised for turning ambiguous quality concerns into clear engineering plans and sustainable technical solutions.
NVIDIA is at the forefront of breakthroughs in Artificial Intelligence, High-Performance Computing, and Visualization. Our teams are composed of driven, innovative professionals dedicated to pushing the boundaries of technology. We offer highly competitive salaries, an extensive benefits package, and a work environment that promotes diversity, inclusion, and flexibility. As an equal opportunity employer, we are committed to fostering a supportive and empowering workplace for all.
Skills Required
- BS or higher in Computer Science, Electronics, or related discipline, or equivalent experience plus 5+ years QA experience
- Proficient in validating web-based UIs and RESTful APIs via code
- Unix/Linux and shell programming skills
- Working command of Python programming language
- Familiarity with networking protocols
- Experience developing test cases and performing failure root cause analysis
- Experience automating manual tests and building/implementing test frameworks
- Experience integrating automation testing into CI/CD and discovery pipelines
- Good command of cloud management systems, Kubernetes, and supporting cloud infrastructure (e.g., Grafana)
- Hands-on experience with LLMs, including prompt engineering, fine-tuning, or integration into QA workflows
- Experience fine-tuning or training models for QA-specific tasks
- Strong QA skills: attention to detail, problem-solving, data analysis, quality standards, and time management
- Excellent written and verbal English communication
- Track record identifying areas for process improvement
- Experience with scalability or performance testing
- Experience building AI systems (RAG, MRC, AI agents) or AI-powered test generation tools
- Experience with NVIDIA GPU hardware
- Experience with data analysis and system monitoring across distributed systems
- Experience with Golang
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.
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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.
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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.
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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
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.”
