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Senior Systems Software Engineer, Kubernetes Node Lifecycle - DGX Cloud Experience: 8 years of experience with a background in systems software, cloud infrastructure, or Kubernetes node engineering. Location: Santa Clara, California, United States Skills: Kubernetes, CAPI, OS image packaging, cloud infrastructure, node lifecycle management, node provisioning, bring-your-own-node workflows, OS image generation, image build pipelines, node image hardening, CIS benchmarks, automated CVE remediation, CI/CD, Golang, Python, GCP, AWS, Azure, OCI, GKE, EKS, AKS, OKE, supply chain security, image signing, provenance attestation, SBOM generation, immutable OS image distributions, Flatcar, Bottlerocket, Azure Linux Company Overview At NVIDIA, the DGX Cloud division merges fresh hardware and software innovations to offer leading accelerated computing solutions for the most challenging AI workloads worldwide. Our team of skilled engineers is committed to addressing major global issues, consistently advancing technology, and making a difference in millions of lives around the world! We are looking for a Senior Systems Software Engineer with strong experience in Kubernetes node engineering, OS image packaging, and cloud infrastructure. The ideal candidate will possess deep hyperscaler-level knowledge across the entire node lifecycle. This covers CAPI providers, bring-your-own-node onboarding, OS image build pipelines, packaging, and nodepool management. They must have the technical depth needed to maintain cluster reliability at frontier AI scale. In this vital role, you will manage the node layer within NVIDIA Kubernetes Engine (NKE). Your work will ensure it scales to fulfill DGX Cloud's two main goals: supporting internal researchers and enabling NCPs. Are you prepared to innovate What you'll be doing: Direct the building and refinement of CAPI providers for NVIDIA Kubernetes Engine, maintaining steady, consistent, and scalable node provisioning across DGX Cloud and NCP environments. Develop and maintain bring-your-own-node workflows that allow customers to integrate different NVIDIA hardware into NKE clusters while ensuring high operational consistency. Coordinate OS image generation, packaging, deployment, and update processes for NKE nodes. Ensure images are fine-tuned for NVIDIA GPU workloads and satisfy enterprise- and cloud-grade security and compliance criteria. Develop and sustain node image hardening pipelines, incorporating CIS benchmarks, automated CVE remediation, and promotion gates connected to security posture. Develop and maintain automated test suites for node images. These tests verify accuracy across Kubernetes versions and NVIDIA hardware configurations. This process occurs prior to production deployment and facilitates continuous validation through modern CI/CD pipelines. Handle nodepool lifecycle at scale, including provisioning, upgrades, drain and cordon workflows, and seamless node replacement across very large clusters with diverse NVIDIA hardware. Examine, resolve, and determine underlying causes of node-layer faults in production NKE clusters, such as those involving image configuration, driver packaging, kubelet operation, and hardware activation, and review and optimize the node layer in real-world high-scale scenarios. Partner with upstream communities including Cluster API, Kubernetes, and CNCF projects to establish node provisioning and lifecycle standards in accordance with NKE requirements. Communicate your progress and findings at internal and external gatherings such as KubeCon and GTC. What we need to see: Bachelors or Masters degree in Engineering (Electrical, Computer Engineering, Computer Science) or equivalent experience. Deep expertise in Cluster API (CAPI), including provider development and full machine lifecycle from provisioning to deletion. Extensive experience with OS image build pipelines, node image packaging, and delivery systems for Kubernetes nodes (for example image-builder, containerd, cloud-init, packer). Practical experience with bring-your-own-node models and integrating diverse hardware into live Kubernetes environments, including large-scale nodepool lifecycle management and upgrades. Strong understanding of kubelet configuration, node bootstrap, and the Kubernetes node registration lifecycle. Experience with node image security, including vulnerability scanning, patch automation, and compliance gating as part of image build pipelines. Proficiency in Golang and/or Python, and hands-on experience with at least one major public cloud provider (GCP, AWS, Azure, OCI or equivalent). Ways to stand out from the crowd: Direct experience building or maintaining node image pipelines for a hyperscaler Kubernetes distribution (GKE, EKS, AKS, OKE, or equivalent). Experience with supply chain security and hardening for node images, including image signing, provenance attestation, SBOM generation, CIS .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.
