NVIDIA
AI
SeniorSystemSoftwareEngineer-DevOpsandInfrastructureAutomation
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“Senior System Software Engineer - DevOps and Infrastructure Automation at NVIDIA. Skills: DevOps, Infrastructure Automation, Kubernetes, CI/CD, IaC, Observability, Linux Systems Programming, Python, Bash. Design, build, and operate the infrastructure backbone powering AI inference products. Own Kubernetes deployments end-to-end across cloud and on-prem: runbooks, canary checks, post-deploy validation, and rollbacks when needed”
What You'll Achieve.
building better tools to deploy and manage this infrastructure; forge the next generation of compute infrastructure; make a lasting impact; reliable, performant, and scalable at every layer!; observability that actually tells the truth about platform health; clean, actionable handoffs to engineering; chip away at toil; streamline end-to-end deployment!; measurable improvements
Industry & Context.
chip away at toil; lead first-level incident triage; Demonstrated ability to debug complex issues spanning kernel modules, container runtimes, and distributed networking
on-call
What They're Looking For.
Must Have
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, CI/CD chops (GitLab CI, GitHub Actions), Git-based workflows, Linux systems programming, scripting in Python and Bash, IaC fluency (Terraform, Ansible, Helm, Crossplane), containerization depth (Docker, containerd, OCI), Proven reliability ownership — SLOs/SLIs, on-call, incident response, and post-incident reviews that drive measurable improvements, hands-on experience with observability stacks like Prometheus, Grafana, and Loki, A clear communicator who writes runbooks people actually use!
Nice to Have
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, 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
What You'll Do.
and operate the infrastructure backbone powering AI inference products
Own Kubernetes deployments end-to-end across cloud and on-prem: runbooks
post-deploy validation
and rollbacks when needed
Architect CI/CD pipelines for automated build
and release of inference libraries and their container-based software stacks
Build observability that actually tells the truth about platform health — dashboards
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
and chip away at toil using GitHub Actions
Own the security posture for infrastructure components: vulnerability scans
and compliance with internal policies
Collaborate closely with deep learning framework engineers
and platform architects to streamline end-to-end deployment
How You'll Work.
Team & Collaboration
working alongside a team of passionate and skilled engineers; Collaborate closely with deep learning framework engineers, compiler teams, and platform architects to streamline end-to-end deployment!
Communication Scope
writes runbooks people actually use!
Full Job Description
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. **What you 'll be doing:** * 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! **What we need to see:** * 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 h
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