DSX NVCRE Senior Engineer

Posted 9 Hours Ago
Be an Early Applicant
3 Locations
In-Office or Remote
184K-357K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Design, build, and operate Go and Python tooling that validates GPU clusters across Kubernetes and Slurm. Develop certification systems, benchmarks, workload catalogs, provenance and evidence pipelines, platform integrations, and signed auditable results. Investigate performance and correctness issues across distributed AI infrastructure, contribute to open-source projects, and author technical documentation. The role requires deep Linux, container, Kubernetes, HPC, GPU, and distributed-systems expertise, with collaboration across internal teams and cloud partners.
Summary Generated by Built In

NVIDIA is seeking a Senior Software Engineer to join our DSX team to build and operate the tooling that verifies AI infrastructure is ready for production. You will work on cluster readiness and certification software, including the open-source Cluster Readiness Engine, that validates GPU clusters across Kubernetes and Slurm, measures real training and inference performance, and produces evidence customers and partners can trust. We partner with the world's most innovative AI companies and cloud providers to make sure their infrastructure performs as designed before their most important workloads land on it.

What you will be doing:

  • Shape the Future of Validation. Design and build the systems that turn cluster validation from a manual exercise into repeatable, verifiable software. Your work will directly shape how NVIDIA and its partners qualify new GPU platforms and deployments.
  • Core Development. Design and implement cluster readiness and certification tooling in Go and Python that runs on Kubernetes and Slurm, including controllers, operators, CLIs, and scheduler integrations.
  • Benchmarking & Workloads. Build a catalog of benchmarks and real workloads spanning communication (NCCL), compute, storage, diagnostics, training, inference, reliability, and security, with per-architecture expectations and clear pass/fail criteria.
  • Data Provenance. Develop provenance and evidence pipelines that capture what actually ran, where it ran, and under which configuration, and that produce signed, auditable results.
  • Platform Integration. Bring new GPU platforms, schedulers, and fabrics under test, from first bring-up on a single node through multi-node validation on production clusters.
  • Cross-functional Collaboration.Collaborate with internal platform, networking, and solutions teams and with cloud partners to root-cause performance and correctness gaps found by the tooling, and feed fixes back into platforms and reference architectures.
  • Open Source Contribution. Contribute to the open-source Cluster Readiness Engine and related projects, including design documents, documentation, and reviews.
  • Technical Authorship. Author design documents, runbooks, and operational guides that codify how clusters are validated and how results are interpreted.

What we need to see:

  • BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
  • 8+ years of software engineering experience building infrastructure or systems software, ideally for large-scale compute, cloud, or HPC environments.
  • Strong programming skills in Go and Python, with experience shipping production-quality services, controllers, or command-line tools.
  • Deep familiarity with Linux systems, containers, and Kubernetes, including writing operators or controllers against the Kubernetes API.
  • Working knowledge of HPC schedulers such as Slurm and of GPU cluster fundamentals: drivers, NCCL, InfiniBand or RoCE fabrics, and GPU health tooling such as DCGM.
  • Experience benchmarking or validating distributed systems, with a rigorous approach to measurement, reproducibility, and evidence.
  • Demonstrated ability to drive complex technical investigations to root cause and to communicate findings clearly to engineering and leadership audiences.

Ways to stand out from the crowd:

  • Hands-on experience running or tuning large-scale distributed training or inference workloads with frameworks such as PyTorch, Megatron, NeMo, vLLM, SGLang, or TensorRT-LLM.
  • Experience with the NVIDIA ecosystem, including DGX systems, GB200/GB300 platforms, CUDA, NVLink and NVSwitch fabrics, and NVIDIA networking.
  • Prior contributions to open-source infrastructure projects, especially Kubernetes operators, scheduler plugins, or benchmarking frameworks.
  • Background in software supply-chain security, attestation, or signed artifacts, and in building audit-grade evidence into systems.
  • Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry), GitOps workflows, and infrastructure as code for GPU clusters.

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. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is looking for phenomenal people like you to help us accelerate the next wave of artificial intelligence. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and dedicated people in the world working for us.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 1, 2026.

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.

Skills Required

  • BS, MS, or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, a related technical field, or equivalent experience
  • 8+ years of software engineering experience building infrastructure or systems software
  • Strong programming skills in Go and Python
  • Experience shipping production-quality services, controllers, or command-line tools
  • Deep familiarity with Linux systems and containers
  • Deep familiarity with Kubernetes, including writing operators or controllers against the Kubernetes API
  • Working knowledge of HPC schedulers such as Slurm
  • Working knowledge of GPU cluster fundamentals, including drivers, NCCL, InfiniBand or RoCE fabrics, and DCGM
  • Experience benchmarking or validating distributed systems with rigorous measurement, reproducibility, and evidence practices
  • Ability to drive complex technical investigations to root cause and communicate findings clearly to engineering and leadership audiences
  • Hands-on experience running or tuning large-scale distributed training or inference workloads with PyTorch, Megatron, NeMo, vLLM, SGLang, or TensorRT-LLM
  • Experience with NVIDIA DGX systems, GB200 or GB300 platforms, CUDA, NVLink, NVSwitch, or NVIDIA networking
  • Contributions to open-source infrastructure projects, especially Kubernetes operators, scheduler plugins, or benchmarking frameworks
  • Background in software supply-chain security, attestation, signed artifacts, or audit-grade evidence systems
  • Familiarity with Prometheus, Grafana, OpenTelemetry, GitOps workflows, and infrastructure as code for GPU clusters

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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