Description
NVIDIA is seeking a Senior Software Engineer to work on its AI Ops platform, which is used for large-scale AI infrastructure. The platform is deployed across various environments, ranging from a single node to tens of thousands of GPUs.
Job Description:
As a Senior Software Engineer, you will be responsible for making the platform deployable, operable, and scalable across customer-controlled environments. Your tasks will include:
- Building the deployment platform, including packaging, configuration, environment validation, upgrade paths, rollback flows, and runtime safeguards
- Ensuring deployability across Kubernetes, cloud, on-prem, and restricted or air-gapped environments
- Designing and improving mechanisms for installability and supportability at scale, including Helm charts, deployment manifests, configuration schemas, dependency validation, readiness checks, and operational tooling
- Partnering with backend and feature teams to ensure services are production-ready on day 1 and remain correct on day 400
- Improving platform resilience, including designing for partial failure, misconfiguration, restarts, state loss, and degraded environments
- Turning scale and benchmark findings into concrete deployment guidance, sizing recommendations, and supported topologies
- Helping evolve the platform from legacy deployment models toward a modern Kubernetes-based operational model
Requirements:
- B.Sc. or M.Sc. in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
- 5+ years of software engineering experience in platform, infrastructure, runtime, or deployability-focused roles
- Hands-on experience with Kubernetes and production deployment tooling such as Helm, operators, or GitOps workflows
- Strong programming and automation skills in one or more of Go, Python, Bash, or similar
- Experience shipping software that other organizations install and operate themselves
- Practical experience with distributed systems and production services that fail independently and must remain correct under partial failure
- Strong debugging skills across Linux, networking, containers, and service runtime behavior
- Experience with CI/CD, release engineering, environment validation, or infrastructure-as-code
Nice to Have:
- Experience delivering software into on-prem, customer-controlled, or air-gapped environments
- Experience with Kubernetes deployability at scale, including upgrades, rollback, configuration management, and day-2 operations
- Familiarity with telemetry, observability, monitoring, or AI/HPC infrastructure
- Experience turning operational pain points into productized solutions, such as pre-install validation, deployment standards, support tooling, or automated recovery paths
- A systems mindset: ability to move comfortably between code, runtime, infrastructure, and customer reality