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NVIDIA

Designs the chips and software that power AI

Senior Software Engineer

Backend EngineeringConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • A BS, MS, or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent experience.
  • Eight or more years of software engineering experience building infrastructure or systems software, ideally for large-scale compute, cloud, or high-performance computing 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 high-performance computing schedulers such as Slurm and GPU cluster fundamentals, including 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.
  • Ability to drive complex technical investigations to root cause and communicate findings clearly to engineering and leadership audiences.

Responsibilities

  • Design and build systems that turn cluster validation into repeatable, verifiable software, helping qualify new GPU platforms and deployments.
  • Design and implement cluster readiness and certification tooling in Go and Python that runs on Kubernetes and Slurm, including controllers, operators, command-line interfaces, and scheduler integrations.
  • 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.
  • Develop provenance and evidence pipelines that capture what ran, where it ran, and under which configuration, and produce signed, auditable results.
  • Bring new GPU platforms, schedulers, and fabrics under test, from first bring-up on a single node through multi-node validation on production clusters.
  • 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.
  • Contribute to the open-source Cluster Readiness Engine and related projects, including design documents, documentation, and reviews.
  • Author design documents, runbooks, and operational guides that codify how clusters are validated and how results are interpreted.

Desired Qualifications

  • 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 experience building audit-grade evidence into systems.
  • Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry), GitOps workflows, and infrastructure as code for GPU clusters.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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