NVIDIA's Deep Learning Libraries Group is seeking excellent software engineers to enable the next wave of NVIDIA’s highest performing deep learning libraries.

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The role spans multiple products, including cuDNN and FlashInfer. The mission is to design and develop scalable, modular infrastructure that streamlines development, build, and test across NVIDIA’s diverse set of platforms, from datacenter to autonomous vehicles. 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.

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What you'll be doing:

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• Designing and developing software for testing and analysis of our codebases
• Building scalable automation for build, test, integration, and release processes for publicly distributed deep learning libraries
• Developing throughout the software stack, from the user experience down to the cluster and database layers
• Configuring, maintaining, and building upon deployments of industry-standard tools (e.g. Kubernetes, Jenkins, Docker, CMake, Gitlab, Jira, etc)
• Advancing innovative in those industry-standard tools and upstreaming contributions to the open source community

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What we need to see:

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• Bachelors or Masters in Computer Science or Computer Engineering (or equivalent experience).
• Strong programming skills in Python (or similar) and familiarity with C/C++ development
• Experience setting up, maintaining, and automating continuous integration systems
• Proficiency in SCM (e.g. Git, Perforce) and build systems (e.g. Make, CMake, Bazel)
• A pragmatic approach to solving problems collaboratively with a passion for “it just works” automation to enable team members

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Ways to stand out from the crowd:

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• Experience designing and developing automation in Jenkins, Gitlab CI/CD, or Github Actions and background with distributed systems and cluster/cloud computing (e.g. Slurm, containers, Kubernetes, etc)
• Experience designing and developing unit and integration test frameworks with hands-on experience using code coverage and static code analysis tools
• Success leading a team of engineers and/or experience as an active contributor to a software project involving many developers
• Knowledge of GPU computing systems and experience with mobile/embedded platforms and multiple operating systems (Ubuntu, CentOS, Windows, L4T, or similar)
• Track record of identifying useful new technologies and incorporating them into SW development flows

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