The Role
Design, operate, and scale CI/CD infrastructure for NVIDIA's deep learning compiler stacks. Improve CI reliability, reproducibility, diagnostics, and automation; apply AI/agent workflows to speed triage and build self-service CI platforms across GPUs and accelerators.
Summary Generated by Built In
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:
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.
Skills Required
- BS, MS, or PhD in Computer Science, Computer/Electrical Engineering, Mathematics, or related field (or equivalent experience)
- 5+ years 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 (pipeline orchestration, runner/compute management, artifacts, observability)
- Strong software engineering skills with ability to design, implement, and debug distributed systems end-to-end (Python required)
- Familiarity with edge devices (SoC, e.g., NVIDIA Tegra) and host-target debugging/automation nuances
- Proven track record designing, building, and deploying AI/LLM-based systems in engineering workflows with measurable impact
- Experience operating CI for DL/GPU environments, including multi-GPU / multi-node workloads on Slurm, Kubernetes, or cloud platforms
- Familiarity with compiler IRs and infrastructure such as LLVM/MLIR and TensorRT IR for testing, debugging, and validation
- Experience building AI/agent-based systems for CI improvements (intelligent test selection, automated triage, regression localization)
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
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The Company
What We Do
NVIDIA’s invention of the GPU in 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, NVIDIA is increasingly known as “the AI computing company.”
