Senior Engineer

NVIDIA

. Design and build systems that turn cluster validation into repeatable, verifiable software .

Posted 10/31/2026full-timeRemote • United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing cluster readiness and certification tooling using Go and Python within Kubernetes environments. Proficient in benchmarking distributed systems and integrating GPU platforms while ensuring software supply-chain security and observability.

Highest-signal resume keywords

Go ProgrammingPython ProgrammingKubernetes API DevelopmentHPC Scheduler KnowledgeDistributed Systems Benchmarking

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Hard Skills

Software EngineeringInfrastructure DevelopmentCluster ValidationEvidence PipelinesGPU Cluster FundamentalsLarge-Scale Distributed TrainingProduction-Quality ServicesCommand-Line ToolsLinux SystemsOpen-Source Contributions

Soft Skills

Technical InvestigationClear Communication

Tools & Technologies

KubernetesSlurmPrometheusGrafanaOpenTelemetryNVIDIA EcosystemCUDANVLinkNVSwitchGitOps Workflows

Industry Keywords

Cloud ComputingHigh-Performance ComputingSoftware Supply-Chain SecurityAudit-Grade EvidenceBenchmarking Frameworks

Tech Stack

Tools & technologies

CloudDistributed SystemsGrafanaKubernetesLinuxNode.jsPrometheusPythonPyTorchGo

About the role

Key responsibilities & impact

Design and build systems that turn cluster validation into repeatable, verifiable software
Design and implement cluster readiness and certification tooling in Go and Python running 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, with per-architecture expectations and pass/fail criteria
Develop provenance and evidence pipelines that capture execution and configuration details and produce signed, auditable results
Integrate new GPU platforms, schedulers, and fabrics 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

What you’ll need

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

Benefits

Comp & perks

Equity
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