Senior System Software Engineer - DevOps and Infrastructure Automation

NVIDIA•Seattle, CA

About The Position

Become a Senior System Software Engineer on NVIDIA's AI Inference Operations Team, focusing on DevOps and Infrastructure Automation. Join a company revolutionizing computer graphics, PC gaming, and accelerated computing. You will be working alongside a team of passionate and skilled engineers who are continuously building better tools to deploy and manage this infrastructure. With your help, we will forge the next generation of compute infrastructure. If you thrive at the intersection of systems programming, cloud-native infrastructure, and developer productivity, this is your opportunity to make a lasting impact at a leading technology company.

Requirements

BS/MS in CS/CE or equivalent experience, plus 7+ years operating production distributed systems (SRE / DevOps / Platform Ops).
Deep Kubernetes expertise — components, subsystems, on-prem setup, and hands-on debugging of telemetry-heavy microservices across AWS, Azure, GCP, and on-prem.
Strong CI/CD chops (GitLab CI, GitHub Actions), Git-based workflows, Linux systems programming, and scripting in Python and Bash.
IaC fluency (Terraform, Ansible, Helm, Crossplane) and containerization depth (Docker, containerd, OCI).
Proven reliability ownership — SLOs/SLIs, on-call, incident response, and post-incident reviews that drive measurable improvements — backed by hands-on experience with observability stacks like Prometheus, Grafana, and Loki.
A clear communicator who writes runbooks people actually use!

Nice To Haves

MLOps experience — crafting, deploying, and operating machine learning pipelines end to end.
Experience in open-source development workflows and community engagement on projects like Triton Inference Server or ONNX Runtime.
Familiarity with GPU software stacks — CUDA, cuDNN, TensorRT, and inference serving frameworks.
Experience building custom test automation frameworks and using data-driven metrics to improve platform health and developer efficiency.
Demonstrated ability to debug complex issues spanning kernel modules, container runtimes, and distributed networking.

Responsibilities

Design, build, and operate the infrastructure backbone powering AI inference products — reliable, performant, and scalable at every layer!
Own Kubernetes deployments end-to-end across cloud and on-prem: runbooks, canary checks, post-deploy validation, and rollbacks when needed.
Architect CI/CD pipelines for automated build, test, packaging, and release of inference libraries and their container-based software stacks.
Build observability that actually tells the truth about platform health — dashboards, logs, metrics, automated checks — and lead first-level incident triage with clean, actionable handoffs to engineering.
Manage cloud and on-prem environments with infrastructure-as-code (Terraform, Ansible, Helm, Crossplane), and chip away at toil using GitHub Actions, GitLab CI, and custom tooling.
Own the security posture for infrastructure components: vulnerability scans, CVE remediation, and compliance with internal policies.
Collaborate closely with deep learning framework engineers, compiler teams, and platform architects to streamline end-to-end deployment!