NVIDIA
AI
InfrastructureSoftwareEngineer,DeepLearningLibraries
Neural analysis suggests this role is
optimal for Mid candidates.
“Infrastructure Software Engineer, Deep Learning Libraries at NVIDIA. Skills: Infrastructure Software Engineering, Deep Learning Libraries, AI agents. Design and develop software for testing. Develop software for analysis of codebases”
What You'll Achieve.
enable next wave of NVIDIA's highest performing deep learning libraries; design and develop scalable, modular infrastructure; streamline development, builds, and tests; deliver world's fastest deep learning platforms; improve development velocity
Industry & Context.
address needs from open-source community; challenge
What They're Looking For.
Must Have
Masters Degree in Computer Science or Computer Engineering or equivalent experience, 3+ years of relevant experience, programming skills in Python (or similar), familiarity with C/C++ development, Experience setting up, maintaining, and automating continuous integration systems, Extensive experience in AI agents technology, Fluency in SCM (e.g. Git, Perforce), Fluency in build systems (e.g. Make, CMake, Bazel)
Nice to Have
Experience designing and developing automation in Jenkins with Groovy (or similar), Background with distributed systems, Background with cluster/cloud computing, 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
What You'll Do.
Design and develop software for testing
Develop software for analysis of codebases
Build scalable automation for build processes
Build scalable automation for test processes
Build scalable automation for integration processes
Build scalable automation for release processes
Automate software development cycle
Configure industry-standard tools
Maintain industry-standard tools
Build upon deployments of industry-standard tools
Advance state of the art in industry-standard tools
How You'll Work.
Team & Collaboration
Join technically diverse team; Work with product team
Full Job Description
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
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