NVIDIA DGX Cloud is developing and managing large-scale GPU infrastructure for AI research and production workloads. We are looking for Senior Reliability Engineers to help build the automation, tooling, and operational systems that make GPU clusters reliable, scalable, and safe to run. This role is part of a production engineering team passionate about Kubernetes-based infrastructure, GPU cluster operations, reliability, automation, GitOps, and Day 2 operability across DGX Cloud environments.
What you’ll be doing:
Build and operate automation for large-scale Kubernetes clusters across NVIDIA Cloud Partners (NCP) and on-prem environments.
Develop tools and services for provisioning, validation, upgrades, monitoring, repair, and cluster lifecycle operations.
Improve Day 0 / Day 1 / Day 2 workflows for cluster bringup, handoff, and production operations.
Define SLOs/SLIs, monitor error allowances, and streamline reporting
Reduce manual production touches through APIs, GitOps, automation, and agent-assisted workflows.
Participate in on-call, incident response, debugging, and durable follow-up work.
Partner with platform, storage, networking, security, and workload teams to make infrastructure production-ready.
What we need to see:
8+ years of experience building or operating production infrastructure.
Strong programming skills in Python, Go, or similar.
Expert-level knowledge with Linux, Kubernetes, containers, cloud infrastructure, or infrastructure automation.
Solid grasp of SRE principles, such as SLOs, SLIs, error budgets, and incident management.
Ability to troubleshoot distributed systems in production.
Experience building and operating comprehensive observability stacks (monitoring, logging, tracing) using tools like OpenTelemetry, Prometheus, Grafana, ELK Stack, Lightstep, Splunk, etc.
Clear communication and ability to work across teams.
BS/MS in Computer Science or equivalent experience.
Ways to stand out from the crowd:
Experience with GPU infrastructure, Kubernetes operators, GitOps, Terraform, ArgoCD, or fleet automation.
Experience with SLOs, on-call, incident response, observability, and reliability practices.
Experience operating and resolving problems in production AI inference workloads across the model-to-GPU stack, including vLLM, SGLang, PyTorch, TensorRT-LLM, NVIDIA Dynamo, CUDA, NCCL, and GPU performance analysis.
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. We have some of the most forward-thinking and hard-working people on the planet working for us. If you're creative, hard-working and self-motivated, we want to hear from you!
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Similar Jobs
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