3 days ago
Shanghai, ChinaSenior
Responsibilities
- Define test strategies, risk-based coverage, acceptance criteria, and release validation for cloud platforms and GPU-accelerated services.
- Develop scalable test frameworks, simulation environments, mock services, and workload generators.
- Build functional, integration, end-to-end, reliability, performance, and security test coverage.
- Investigate complex failures using logs, metrics, traces, and controlled experiments, and verify fixes with development and SRE teams.
- Integrate automated validation into CI/CD pipelines and communicate quality trends, coverage gaps, and release risks.
- Build and improve AI agents and agentic QA workflows for test planning, development, execution, failure analysis, and reporting.
- Audit AI-generated outputs and drive cross-team quality improvements through tooling, architecture contributions, code reviews, and knowledge sharing.
Requirements
- Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
- At least 3 years of relevant software development or test development experience involving cloud services, infrastructure software, or distributed systems.
- Strong Python programming skills, shell scripting proficiency, and experience developing maintainable automation frameworks or engineering tools.
- Understanding of Linux, networking, HTTP/REST APIs, concurrency, timeouts, retries, consistency, and failure recovery in distributed systems.
- Hands-on experience with containers, Kubernetes, workload deployment, troubleshooting, and automated regression testing.
- Experience designing reproducible tests and diagnosing failures across software and infrastructure components using technical evidence.
- Experience building AI agents or LLM-powered automation and auditing and improving their outputs.
- Preferred qualifications include simulation, emulation, or fault-injection tooling; resource scheduling and GPU computing knowledge; multi-tenant, traffic-management, or model-serving validation; sustained AI initiatives; and demonstrated technical influence.
Tech Stack
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.