We are now looking for an Infrastructure Software Engineer for Deep Learning Libraries!
NVIDIA's Deep Learning Libraries Group is seeking excellent software engineers to enable the next wave of NVIDIA’s highest performing deep learning libraries. The role focuses on NVIDIA's open-source products such as CUTLASS. The mission is to design and develop scalable, modular infrastructure that streamlines development, builds, and tests across NVIDIA’s diverse set of platforms, and address the needs from the open-source community, with the cutting-edge AI technology. Join our technically diverse team of software engineers and infrastructure experts to design the systems that enable NVIDIA to stay ahead of the competition as we deliver the world's fastest deep learning platforms.
What you'll be doing:
Designing and developing software for testing and analysis of our codebases
Building scalable automation for build, test, integration, and release processes for open-source products
Developing and deploying AI agents and similar technology to automate the end-to-end software development cycle
Configuring, maintaining, and building upon deployments of industry-standard tools (e.g. Kubernetes, Jenkins, Docker, CMake, Gitlab, Jira, etc.)
Advancing the state of the art in those industry-standard tools
What we need to see:
A Masters Degree in Computer Science or Computer Engineering or equivalent experience.
3+ years of relevant experience
Strong programming skills in Python (or similar) and familiarity with C/C++ development
Experience setting up, maintaining, and automating continuous integration systems (e.g. Jenkins, GitHub Actions, GitLab pipelines, Azure DevOps)
Extensive experience in AI agents technology
Fluency in SCM (e.g. Git, Perforce) and build systems (e.g. Make, CMake, Bazel)
Ways to stand out from the crowd:
Experience designing and developing automation in Jenkins with Groovy (or similar)
Background with distributed systems and cluster/cloud computing, especially with Kubernetes
Experience designing and developing unit and integration test frameworks
Close follow the latest trend in AI industry
Track record of identifying useful new technologies and incorporating them into SW development flows
This is an opportunity to have a wide impact at NVIDIA by improving development velocity across our many AI/DL/Compute Software projects. Are you creative, driven, and autonomous? Do you love a challenge? If so, we want to hear from you!
Skills Required
- Masters Degree in Computer Science or Computer Engineering or equivalent experience
- 3+ years of relevant experience
- Strong programming skills in Python (or similar)
- Familiarity with C/C++ development
- Experience setting up and automating continuous integration systems
- Extensive experience in AI agents technology
- Fluency in SCM (e.g. Git, Perforce)
- Experience in build systems (e.g. Make, CMake, Bazel)
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.
NVIDIA Insights
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.”
