Cloud & Customer Solutions Engineer, DC GPU

Amd

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Bellevue, WA
Salary: $204,000–$306,000 / yr
Posted: 60 days ago
Freshness: Confirmed live today
Closes: Aug 27, 2027
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $196k

This role $255k

$134k $324k

below market most similar roles pay here above market

This role pays more than 87% of similar roles. Most pay $156,550–$235,750 — the blue band above. At the midpoint, this role pays about $255k versus about $196k for comparable roles.

Based on 240 similar postings.

Employer

About Amd

AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors

Amd currently has 496 open roles on FindRole.

Listed pay typically runs $172,000–$258,000 across 496 roles with salary data.

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At a glance

TL;DR · Cloud & Customer Solutions Engineer, DC GPU

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The Cloud & Customer Solutions Engineer - DC GPU joins the Data Center GPU organization within the Applied AI team. This role involves embedding directly with strategic AI customers, including frontier labs and NeoCloud providers, to manage AMD Instinct GPU clusters from initial bring-up to sustained production excellence. You will write production code, operate live clusters, and perform root-cause analysis for production incidents. Key responsibilities include deploying and tuning large-scale training and inference stacks, building observability and benchmarking tools, and contributing upstream fixes to ROCm. Required technical skills include Python, systems languages, Kubernetes, Slurm, RCCL, and high-performance networking like RoCE or InfiniBand. You will solve complex problems regarding distributed training hangs and workload optimization across cloud, NeoCloud, and bare-metal environments to ensure autonomous customer operation.

Skills

ROCm Kubernetes Slurm Python vLLM SGLang RCCL Prometheus Grafana AWS Azure GCP InfiniBand RoCE CUDA RAG Distributed Training Inference Serving Bare-metal

What you'll do

Own customer deployments end-to-end including cluster bring-up, certification, workload onboarding, and performance validation.
Deploy and tune large-scale training and inference stacks across cloud, NeoCloud, and bare-metal environments.
Lead root-cause analysis and resolution for production incidents on customer clusters, including Sev-1 responses.
Deploy agentic AI solutions into customer environments and manage their production behavior.
Build observability, benchmarking, and validation tooling to certify and maintain production-ready clusters.
Transfer operational capability to customer teams through documentation, runbooks, and hands-on enablement.
Convert field findings into upstream contributions for ROCm, serving frameworks, and reference architectures.

What we're looking for

Deploy and tune large-scale training and inference stacks including ROCm, vLLM, SGLang, RCCL, Kubernetes, and Slurm.
Lead root-cause analysis and resolution for production incidents on customer clusters, including Sev-1 responses.
Own end-to-end customer deployments including cluster bring-up, performance validation, and sustained production operation.
Build observability, benchmarking, and validation tooling to certify clusters as production-ready.
Convert field findings into upstream contributions such as ROCm issues, patches, and reference-architecture updates.
5+ years of production software or infrastructure engineering experience (preferred).
Hands-on experience with GPU compute at scale, including distributed training and performance debugging (preferred).
Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience (preferred).

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