Senior Infrastructure and Build Systems Engineer

NVIDIA is seeking a Senior Infrastructure and Build Systems Engineer for their AI TensorRT-LLM team. The role involves taking ownership of infrastructure and DevOps landscapes, managing CI/CD pipelines, and collaborating with cross-functional teams to enhance efficiency and reliability in deep learning deployment.

Responsibilities:

• Building and maintaining infrastructure from first principles needed to deliver TensorRT LLM
• Maintain CI/CD pipelines to automate the build, test, and deployment process and build improvements on the bottlenecks
• Managing tools and enabling automations for redundant manual workflows via Github Actions, Gitlab, Terraform, etc
• Enable performing scans and handling of security CVEs for infrastructure components
• Improve the modularity of our build systems using CMake
• Use AI to help build automated triaging workflows
• Extensive collaboration with cross-functional teams to integrate pipelines from deep learning frameworks and components is essential to ensuring seamless deployment and inference of deep learning models on our platform

Requirements:

• Masters degree or equivalent experience
• 3+ years of experience in Computer Science, computer architecture, or related field
• Ability to work in a fast-paced, agile team environment
• Excellent Bash, CI/CD, Python programming and software design skills, including debugging, performance analysis, and test design
• Experience with CMake
• Background with Security best practices for releasing libraries
• Experience in administering, monitoring, and deploying systems and services on GitHub and cloud platforms
• Highly skilled in Kubernetes and Docker/containerd
• Automation expert with hands-on skills in frameworks like Ansible & Terraform
• Experience in AWS, Azure or GCP
• Experience contributing to a large open-source deep learning community - use of GitHub, bug tracking, branching and merging code, OSS licensing issues handling patches, etc
• Experience in defining and leading the DevOps strategy (design patterns, reliability and scaling) for a team or organization
• Experience driving efficiencies in software architecture, creating metrics, implementing infrastructure as code and other automation improvements
• Deep understanding of test automation infrastructure, framework and test analysis
• Excellent problem solving abilities spanning multiple software (storage systems, kernels and containers) as well as collaborating within an agile team environment to prioritize deep learning-specific features and capabilities within Triton Inference Server, employing advanced troubleshooting and debugging techniques to resolve complex technical issues