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Cloud & Customer Solutions Engineer - DC GPU

AMD· Engineering
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Bellevue, WA$204kFull-timePosted 1mo ago

About the role

Cloud & Customer Solutions Engineer on AMD's Applied AI team embedded with strategic AI customers to bring AMD Instinct GPU clusters from delivery to sustained production excellence, owning customer outcomes end-to-end including cluster bring-up, workload deployment, production incidents, and transfer of operational capability.

Responsibilities

  • Own customer deployments end-to-end: cluster bring-up and burn-in, production readiness certification, workload onboarding, performance validation, and sustained production operation on AMD Instinct GPU fleets
  • Deploy and tune large-scale training and inference stacks (ROCm, vLLM, SGLang, RCCL, Kubernetes, Slurm) against customer-specific workloads and SLOs across cloud, NeoCloud, and bare-metal environments
  • Lead root-cause analysis and resolution of production incidents on customer clusters, including Sev-1 response, and drive fixes to permanent closure
  • Deploy agentic AI solutions into customer environments in partnership with Agentic Data Engineers, and own their production behavior within the engagement
  • Build the observability, benchmarking, and validation tooling needed to certify clusters as production-ready and keep them there
  • Transfer operational capability to customer teams — documentation, runbooks, and hands-on enablement — moving customers up the operator-autonomy ladder from assisted operation to independent production ownership
  • Contribute field learnings to the Applied AI team's skills library and engagement memory databases, so deployment knowledge compounds across the practice
  • Convert field findings into upstream contributions — ROCm issues and patches, serving-framework improvements, reference-architecture updates — and provide structured field signal to AMD product, software, and silicon teams

Qualifications

  • 5+ years of production software or infrastructure engineering, including significant time operating or deploying systems in environments you did not build
  • Hands-on experience with GPU compute at scale: cluster deployment, distributed training or high-throughput inference, performance debugging, and workload optimization
  • Strong working knowledge of the modern AI infrastructure stack: Kubernetes and/or Slurm, containerized GPU workloads, collective communication libraries (RCCL/NCCL), high-performance networking (RoCE/InfiniBand), and observability tooling (Prometheus, Grafana)
  • Cloud platform depth (AWS, Azure, GCP, or NeoCloud environments), including hybrid and bare-metal deployment patterns
  • Proficiency in Python and at least one systems language; comfort navigating and modifying large codebases you did not write
  • Working familiarity with LLM application patterns — inference serving, RAG, and agentic workflows
  • Direct customer-facing experience: embedded deployments, technical escalations, on-site engagements, or equivalent
  • Experience with ROCm and AMD Instinct GPUs strongly preferred; deep CUDA-ecosystem experience with demonstrated ability to work cross-platform valued
  • Open-source contribution history in AI/ML infrastructure projects is a plus
  • Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience

Skills

Required:Production engineeringGPU compute at scaleKubernetes and/or SlurmPythonObservability tools
Nice to have:ROCm/NCCL/RCCLSystems programmingLLM application patterns

Benefits

  • Benefits offered are described: AMD benefits at a glance
Last updated Oct 22, 2026. Interested? Apply on the company site.
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