Senior Engineer
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Job Description
Senior Engineer building NVIDIA’s GPU-cluster validation and certification software. Developing Go and Python tooling across Kubernetes and Slurm for production AI infrastructure.
Responsibilities:
- Design and build systems that turn cluster validation into repeatable, verifiable software
- Design and implement cluster readiness and certification tooling in Go and Python on Kubernetes and Slurm, including controllers, operators, CLIs, and scheduler integrations
- Build a catalog of benchmarks and real workloads spanning communication, compute, storage, diagnostics, training, inference, reliability, and security
- Develop provenance and evidence pipelines that capture execution details and produce signed, auditable results
- Bring new GPU platforms, schedulers, and fabrics under test from single-node bring-up through multi-node production-cluster validation
- Collaborate with internal platform, networking, and solutions teams and cloud partners to root-cause performance and correctness gaps
- Contribute to the open-source Cluster Readiness Engine and related projects through design documents, documentation, and reviews
- Author design documents, runbooks, and operational guides for cluster validation and result interpretation
Requirements:
- 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 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 communicate findings clearly to engineering and leadership audiences
- Hands-on experience with 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 building audit-grade evidence into systems
- Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry), GitOps workflows, and infrastructure as code for GPU clusters
Benefits:
- Equity
- Benefits
- Equal opportunity employment
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