Senior Engineer

🕒 Yesterday

🏄 California, Texas, +1 more states – Remote

🏄 California – Remote 🤠 Texas – Remote ☕ Washington – Remote

💵 $184k - $356.5k / year

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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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