Infrastructure and Build Systems Engineer - New College Grad 2026

NVIDIA•Us, CA

1d•Hybrid

About The Position

We are now seeking a Infrastructure and Build Systems Engineer for NVIDIA AI TensorRT-LLM team. This is a unique opportunity to take full ownership of the critical systems that power our engineering innovation. You and the team will be responsible for the entire infrastructure/DevOps landscape, from our CI/CD pipelines to our build systems to product security, driving efficiency and reliability across the organization. You will work with autonomy to design and implement the best solutions and collaborate with external partners to achieve our goals. If you're passionate about infrastructure, automation, observability, and compliance, we want you with us at one of the most innovative companies in the world! What you'll be doing: 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
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.
Support other technical teams in monitoring operating efficiencies of the platform, and responding as needs arise.
Highly skilled in Kubernetes and Docker/containerd.
Automation expert with hands-on skills in frameworks like Ansible & Terraform.
Experience in AWS, Azure or GCP

Nice To Haves

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.

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.