Senior Software Development Engineer in Test, SDET - DGX Cloud
- China, Shanghai
- Full-time
- Posted
Skills
Job description
NVIDIA is advancing accelerated computing and AI to solve some of the world’s most challenging problems. The DGX Cloud team brings together GPU computing, cloud infrastructure, and software engineering to deliver reliable services for demanding AI workloads. We are looking for a Senior Software Development Engineer in Test to drive quality across cloud platforms and complex distributed systems. This role combines hands-on test development with opportunities to build simulation environments, reusable engineering tools, and agentic QA workflows. Working closely with developers, SREs, and quality engineers, you will improve engineering productivity and product quality while delivering measurable business impact.
What you’ll be doing:
•Define and drive test strategies for cloud platforms and GPU-accelerated services, translating requirements and system designs into risk-based coverage, measurable acceptance criteria, and release validation.
•Design and develop scalable test frameworks, simulation environments, mock services, and workload generators to validate deployment and scheduling across diverse infrastructure configurations.
•Build functional, integration, end-to-end, reliability, performance, and security coverage for service lifecycle operations, APIs, access controls, resource isolation, scaling, and failure recovery.
•Investigate complex failures using logs, metrics, traces, and controlled experiments; partner with developers and SREs to identify root causes, verify fixes, and prevent regressions.
•Integrate automated validation into CI/CD pipelines and communicate actionable findings on quality trends, coverage gaps, and release risks.
•Design and build AI agents and agentic QA workflows where appropriate to accelerate test planning, test development, execution, failure analysis, and reporting.
•Audit AI-generated outputs against tests and source evidence, continuously improve accuracy and reliability, and measure impact through reduced engineering effort, earlier defect detection, and faster delivery.
•Drive cross-team quality improvements, contribute to test architecture and code reviews, and share expertise in testing, debugging, and AI-powered engineering practices.
What we need to see:
•Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
•3 years of relevant experience in software development or test development for cloud services, infrastructure software, or distributed systems.
•Strong programming skills in Python, working proficiency in shell scripting, and experience developing maintainable automation frameworks or engineering tools.
•Solid understanding of Linux, networking, HTTP/REST APIs, and distributed-system behavior, including concurrency, timeouts, retries, consistency, and failure recovery.
•Hands-on experience with containers, Kubernetes, and CI/CD pipelines, including workload deployment, troubleshooting, and automated regression testing.
•Experience designing reproducible tests and diagnosing failures across software and infrastructure components using logs, metrics, and other technical evidence.
•Hands-on experience building AI agents or LLM-powered automation using tools, APIs, and relevant context, with the ability to audit outputs for accuracy and systematically improve their quality.
•Ability to independently drive test planning and development, prioritize automation opportunities based on engineering needs and business value, and collaborate effectively across teams, with strong written and spoken English.
Ways to stand out from the crowd:
•Experience building simulation, emulation, or fault-injection tools for infrastructure testing.
•Knowledge of resource scheduling, workload placement, hardware topology, or heterogeneous GPU computing environments.
•Experience validating multi-tenant services, traffic management, or model-serving infrastructure, including performance and reliability at scale.
•A track record of sustained AI initiatives and innovation, turning experiments into adopted solutions with measurable improvements in productivity, product quality, or business outcomes.
•Demonstrated technical influence through reusable tooling, cross-team engineering improvements, and knowledge sharing.