Design, deploy, and operate solutions on Kubernetes for large-scale storage and data platforms, including the manifests, Helm charts, and operators that run them.
Build tools, services, and automation that improve the lifecycle of storage and data systems - from provisioning and configuration through deployment, scaling, and day-2 operations.
Develop and operate telemetry and observability for production systems - metrics, logging, tracing, dashboards, and alerting - so that system health, availability, and latency are measurable and actionable.
Apply strong analytical troubleshooting skills to diagnose and resolve complex issues across distributed, containerized infrastructure.
Work closely with peers and partner teams to improve the lifecycle of services, from inception and design through deployment, operation, and refinement.
Scale systems sustainably through automation, infrastructure-as-code, and CI/CD, and evolve systems by pushing for changes that improve reliability and velocity.
Support services before they go live through activities such as deployment automation, capacity planning, and launch and readiness reviews.
Practice sustainable incident response and postmortems, and participate in an on-call rotation to support production systems.
What We Need To See:
BS degree (or equivalent experience) in Computer Science or related technical field involving coding.
12+ years of practical experience.
Hands-on experience with Kubernetes - deploying, configuring, and operating workloads and solutions on Kubernetes in production.
Experience building tools and services for storage, data, or platform infrastructure, with solid software design fundamentals (algorithms, data structures, complexity analysis) on large-scale Linux-based systems.
Experience building and operating telemetry and observability using tools such as Prometheus, InfluxDB, Grafana, and the Elastic stack.
Strong analytical troubleshooting skills with a systematic, root-cause-driven approach to identifying and resolving complex problems.
Proficiency in one or more of the following: Python, Go, or Java.
Good knowledge of infrastructure configuration management and infrastructure-as-code tools such as Ansible, Chef, Puppet, ArgoCD, Git Pipelines, and Terraform.
Ways to Stand Out from the Crowd:
Customer-first mindset with a focus on customer satisfaction and a passion for ensuring customer success.
Experience with Git, code review, pipelines, and CI/CD. Experience using or running large private and public cloud systems based on Kubernetes, OpenStack, and Docker.
Interest in crafting, analyzing, and fixing large-scale distributed systems, with strong debugging skills and a systematic problem-solving approach.
Experience designing storage- or data-focused tooling and automating their operations at scale.
Thrive in collaborative environments and enjoy working with various teams, and ar
Additional Information
Systems Engineering is an engineering discipline focused on building, automating, and operating the platforms and tooling that deliver large-scale production systems with high efficiency, reliability, and velocity. It combines software and systems engineering practices across infrastructure automation, containerized platforms, storage, telemetry, and observability. Systems engineers are highly specialized and possess expertise across domains such as Kubernetes and container orchestration, infrastructure-as-code, CI/CD, storage systems, monitoring, and analytical troubleshooting. Their responsibilities center on deploying and operating reliable, automated platforms and on building the tools and services that keep storage and data infrastructure healthy and performant.
Our team at NVIDIA ensures that our internal and external facing GPU cloud services are deployed reliably, observable end-to-end, and continuously improved through automation. We enable developers to ship changes safely through repeatable CI/CD pipelines and Kubernetes-based deployments while keeping an eye on capacity, latency, and performance. A core part of this work is an SRE mindset: eliminating manual toil through automation, building self-service tooling, and growing the efficiency of production systems. We use a breadth of tools and approaches to tackle a broad spectrum of problems, and practices such as blameless postmortems, proactive identification of failure modes, and iterative improvement are key to product quality and to an interesting, dynamic day-to-day. Our culture of diversity, intellectual curiosity, problem-solving, and openness is important to our success. Our organization brings together people with a wide variety of backgrounds, experiences, and perspectives. We encourage them to collaborate, think big, and take risks in a blame-free environment. We promote self-direction to work on meaningful projects while striving to build an environment that provides the support and mentorship needed to learn and grow.
Nvidia · Worldwide