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上海市, 中国
Onsite
NVIDIA is the world leader in GPU Computing. We are passionate about markets including 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 Test Development Engineer, you will collaborate with multi-functional groups. SWQA test 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 test 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.
Analyze requirements and design test matrices covering functionality, performance, and edge cases.
Develop test plans and cases; build and maintain automated test suites (API / UI / CLI / E2E) in Python.
Leverage AI-powered tools and agentic workflows to accelerate test generation, triage, and root cause analysis.
Manage the full bug lifecycle — filing, reproduction, and driving multi-functional collaboration to resolution.
Reproduce and verify customer-reported issues to ensure quality before release.
Find opportunities to improve QA efficiency through tooling and automation.
Pursuing Master’s or higher in CS / EE / CE or related field.
Solid software development skills with hands-on project experience;
Proficient in Python; familiar with Unix/Linux and shell scripting.
Experience using AI-powered development tools to boost productivity in real projects.
Familiarity with Docker / Kubernetes and cloud-based test infrastructure.
Familiarity with test methodologies — test design, automation, and defect analysis.
Strong QA instinct — attention to detail, analytical, systematic problem-solver.
Fluent English (written & spoken); takes initiative and collaborates well.
Ability to work full-time, on-site five days per week, for one year.
Built E2E test automation frameworks (pytest, CI/CD integration).
Hands-on with Agentic AI workflows — AI agents for task automation or intelligent testing.
Open-source contributions or visible GitHub portfolio.
Knowledge of LLM evaluation — prompt engineering, accuracy measurement, or AI guardrails.
Prior experience in a fast-paced, ship-focused environment.