Key Facts

Remote From:

China

Category: Infrastructure Engineer

Full time

Senior (5-10 years)

English

Hard Skills

CI/CD Python (Programming Language) Computing Platforms Distributed Computing Jenkins Artifactory Packaging Engineering Observability Edge Computing Multi-Agent Systems +7 more

Other Skills

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Problem Solving
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Communication
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Teamwork

Roles & Responsibilities

BS, MS, or PhD in Computer Science, Computer/Electrical Engineering, Mathematics, or a related field
5+ years of experience designing, scaling, and operating CI/CD, build/release, or developer infrastructure for complex software systems
Proven experience building CI platforms end-to-end using systems such as GitLab CI, Jenkins, or similar tools
Strong software engineering skills (Python required)

Requirements:

Build, maintain, and improve CI infrastructure that supports development, verification, and release of NVIDIA’s deep learning compiler stacks
Improve CI reliability and signal quality by reducing flakes, improving reproducibility, and strengthening diagnostics
Apply automation, AI, and agent-based workflows to reduce manual CI operations and speed up failure triage
Build reusable and self-service CI platforms that support multiple products, projects, model suites, hardware targets, and software configurations

Job description

NVIDIA's invention of the GPU 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, we are increasingly known as “the AI computing company”.
In this role you will work closely with deep learning compiler engineers to own and evolve the CI/CD infrastructure that powers the development lifecycle of NVIDIA's deep learning compiler stacks. Responsibilities include designing and operating scalable CI systems that orchestrate ML workloads across diverse GPU and accelerator environments, deliver reliable correctness and performance signals, and serve as a primary technical point of contact for CI health, new project onboarding, and new architecture bring-up.
What you'll be doing:

Build, maintain, and improve CI infrastructure that supports development, verification, and release of NVIDIA’s deep learning compiler stacks across GPU and accelerator environments
Improve CI reliability and signal quality by reducing flakes, improving reproducibility, strengthening diagnostics, and making correctness and performance failures easier to understand and act on
Apply automation, AI, and agent-based workflows to reduce manual CI operations, speed up failure triage, and improve developer efficiency
Build reusable and self-service CI platforms that support multiple products, projects, model suites, hardware targets, and software configurations while partnering closely with compiler, infrastructure, and release teams

What we need to see:

BS, MS, or PhD (or equivalent experience) in Computer Science, Computer/Electrical Engineering, Mathematics, or a related field
5+ years of experience designing, scaling, and operating CI/CD, build/release, or developer infrastructure for complex software systems
Proven experience building CI platforms end-to-end using systems such as GitLab CI, Jenkins, or similar tools, including pipeline orchestration, compute/runner management, artifact and package systems, and observability, with strong emphasis on reliability, reproducibility, and debuggability
Strong software engineering skills (Python required), with the ability to design, implement, and debug distributed systems end-to-end
Familiarity with edge devices (SOC, e.g. NVIDIA Tegra) in host-target architecture, ability to debug it and knowledge to its automation nuances
Proven track record of designing, building, and deploying AI/LLM-based systems in real engineering workflows, demonstrating skill in evaluating trade-offs, failure modes, maintainability, and measurable impact on developer productivity, signal quality, or operational efficiency

Ways to stand out from the crowd:

Experience crafting and shipping sophisticated AI/agent-based systems that improve continuous integration or developer efficiency. These systems include intelligent test selection, automated triage and routing, regression localization, autonomous remediation, and developer-assist workflows
Experience operating CI for DL/GPU software environments, including multi-GPU / multi-node workloads on Slurm, Kubernetes, or cloud platforms
Familiarity with compiler IRs and infrastructure such as LLVM/MLIR, TensorRT IR, especially in the context of testing, debugging, and validating compiler-driven workloads

With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.