Senior Systems Software Engineer, Accelerated Kubernetes Performance And Scale - DGX Cloud
at Nvidia
USD 184,000-356,500 per year
Used Tools & Technologies
GoRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Kubernetes @ 4
Python @ 6
GCP @ 4
CI/CD @ 4
Distributed Systems @ 4
AWS @ 4
Azure @ 4
Communication @ 4
Networking @ 7
Performance Optimization @ 7
API @ 4
OSS @ 4
GPU @ 4
AI @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. The DGX Cloud organization brings together cutting-edge hardware and software innovation to deliver industry-leading accelerated computing for ambitious AI workloads. The team is searching for a Senior Systems Software Engineer with deep expertise in distributed systems, Kubernetes, containers, and systems performance and scalability. The role focuses on scaling AI infrastructure while minimizing total cost of ownership and reducing cost per token.
Responsibilities
- Lead end-to-end performance and scalability analysis across the Kubernetes-based accelerated runtime stack (control and data planes), including NVIDIA components such as GPU Operator, Network Operator, node-feature-discovery, topograph, dra-driver-nvidia-gpu, and nvsentinel, tracking issues from orchestration down to the metal.
- Design and contribute upstream architectural changes to the Kubernetes control plane and related projects to enable reliable operation at hyperscale cluster sizes.
- Improve container startup and cold-start latency to enable smooth, low-latency inference scaling on Kubernetes across thousands of GPU nodes, ensuring the AI runtime stack scales without creating API server pressure or operational fragility.
- Assess, improve, and contribute to open-source projects that make Kubernetes an outstanding platform for AI workloads (for example, Grove and gateway-api-inference-extension), composing architectures with scalability, resilience, and multi-node training/inference in mind.
- Advance scalability and performance of confidential containers (CoCo) on Kubernetes so encrypted inference workloads meet efficiency and latency requirements in production.
- Use DSX and related large-scale simulation infrastructure to model full AI-factory deployments and validate scalability across thousands of simulated GPUs.
- Collaborate with AI researchers, developers, customers, and upstream communities to design automated, at-scale workload tests (including replay of production agent traces), build monitoring/analysis tooling, and integrate continuous performance and scale testing into modern CI/CD workflows.
- Document methods and results clearly and present findings internally and at industry events (for example, KubeCon, GTC), while engaging with upstream groups (Kubernetes SIG Scalability, CNCF, and NVIDIA OSS communities).
Requirements
- Bachelor’s or Master’s degree in Engineering or equivalent experience (Electrical, Computer Engineering, or Computer Science preferred).
- 8+ years of experience in computer architecture, networking, storage systems, and accelerator-based platforms.
- Expertise in Kubernetes and familiarity with the broader CNCF ecosystem.
- Deep experience with large-scale, parallel, distributed accelerator systems and performance optimization of AI workloads.
- Experience with performance modeling and benchmarking for large-scale systems.
- Proficiency in Golang and/or Python.
- Strong familiarity with the NVIDIA software stack across training and inference.
- Expertise with at least one major public cloud provider (for example, AWS, Azure, GCP, or OCI).
Ways to stand out from the crowd
- Strong operational experience with any one of the Kubernetes distributions.
- Prior experience scaling Kubernetes clusters to ultra-large node and object counts.
- Demonstrated history of working in the open-source community.
- Excellent communication and interpersonal abilities.
- PhD or equivalent experience in relevant areas.
Compensation & Other Details
- The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. Actual base salary will be determined based on location, experience, and pay of employees in similar positions.
- Eligible for equity and benefits.
- Work preference: preference for hybrid work while remaining open to remote arrangements. (#LI-Hybrid)
- Applications accepted at least until June 29, 2026.
- NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.