Senior Site Reliability Engineer in Test, SDET

At NVIDIA, we are at the forefront of technological innovation, pushing the boundaries of AI and accelerated computing. Our team in Shanghai, China is looking for a Senior Site Reliability Engineer focused on Test Environment Management to join us. This is an opportunity to build and operate highly reliable test infrastructure, CI/CD systems, and environments that power validation of NVIDIA enterprise offerings. If you are passionate about reliability engineering, test infrastructure, and AI-scale systems, this role is for you. What you’ll be doing Design, build, operate, and continuously improve reliable, scalable test environments and automation infrastructure that support validation of NVIDIA enterprise offerings. Own end-to-end CI/CD pipelines using GitLab CI, GitHub Actions, and ArgoCD (GitOps) — including pipeline design, reliability, performance, and progressive delivery of test workloads. Manage Software Bills of Materials (SBOMs): generation, continuous monitoring, vulnerability correlation, policy enforcement, and integration into CI/CD and release gate.. Provision, scale, observe, and lifecycle-manage ephemeral and long-lived test environments (Kubernetes-based and hybrid) with strong emphasis on isolation, reproducibility, and rapid recovery. Define and drive reliability practices for test systems: SLIs/SLOs/error budgets for test environments and pipelines, toil reduction, chaos/resilience testing of infrastructure, and automated remediation. Collaborate closely with development and platform teams to triage environment and pipeline failures, perform root-cause analysis, verify fixes, and continuously harden test infrastructure. Apply AI/ML/Agentic techniques and internal tools to accelerate environment provisioning, flaky-test detection, capacity planning, anomaly detection, and overall Quality Assurance velocity. What we need to see MS or PhD in Computer Science, related field, or equivalent experience with 8+ years of professional experience in Site Reliability Engineering, Test Environment Management, CI/CD platform engineering, or software testing infrastructure. Strong proficiency with Linux, shell scripting, and Python (or equivalent automation languages). Hands-on experience designing and operating CI/CD systems with GitLab CI and/or GitHub Actions, practical experience with ArgoCD (or equivalent GitOps tooling) for CD of applications and infrastructure. Solid background in containerization and orchestration (Docker, Kubernetes) and virtualization technologies. Deep understanding of SRE principles: SLIs/SLOs, error budgets, incident response, postmortems, toil elimination, and reliability engineering for complex distributed systems. Experience building and operating test environments (ephemeral, multi-tenant, or production-like) with focus on reliability, isolation, and rapid turnaround. Strong knowledge of QA principles and how test infrastructure enables high-quality software delivery. Comfort working with AI/LLM-related workloads and toolings. Excellent problem-solving skills, clear written and verbal communication, and the ability to collaborate across engineering teams. Self-motivated, proactive, and passionate about learning new technology at scale. Ways to stand out from the crowd Experience operating large-scale Kubernetes platforms and GitOps workflows in production or high-stakes test environments. Background in software supply-chain security, SBOM tooling ecosystems, vulnerability management, and policy enforcement (OPA/Gatekeeper, Kyverno, etc.). Hands-on work with NVIDIA GPU hardware, multi-GPU environments, or accelerated computing infrastructure. Experience with parallel programming, high-performance computing, or large-scale AI model training/inference test harnesses. Track record of applying AI/observability techniques to detect flaky tests, optimize environment utilization, or automate root-cause analysis. Prior experience defining and driving reliability programs (error budgets,