2 months ago
Bellevue, WA, USASenior
Responsibilities
- Own customer deployments from GPU cluster bring-up and burn-in through production readiness certification, workload onboarding, performance validation, and sustained operation.
- Deploy and tune large-scale training and inference stacks across cloud, NeoCloud, and bare-metal environments.
- Lead root-cause analysis and permanent resolution of production incidents, including Sev-1 responses.
- Deploy agentic AI solutions with Agentic Data Engineers and own their production behavior during customer engagements.
- Build observability, benchmarking, and validation tooling for production-readiness certification.
- Transfer operational capability through documentation, runbooks, and hands-on customer enablement.
- Contribute field learnings to the Applied AI team’s skills library and engagement memory databases.
- Convert field findings into upstream ROCm, serving-framework, and reference-architecture improvements and provide feedback to AMD product, software, and silicon teams.
Requirements
- 5+ years of production software or infrastructure engineering experience, including significant experience operating or deploying systems built by others; level is flexible for exceptional candidates.
- Hands-on experience deploying GPU compute clusters at scale, distributed training or high-throughput inference, performance debugging, and workload optimization.
- Strong knowledge of Kubernetes or Slurm, containerized GPU workloads, RCCL or NCCL, RoCE or InfiniBand, and observability tooling such as Prometheus and Grafana.
- Depth with AWS, Azure, GCP, or NeoCloud environments, including hybrid and bare-metal deployment patterns.
- Proficiency in Python and at least one systems language, with the ability to navigate and modify large unfamiliar codebases.
- Familiarity with LLM inference serving, RAG, and agentic workflows sufficient to deploy and troubleshoot them in customer environments.
- Direct customer-facing experience through embedded deployments, technical escalations, on-site engagements, or equivalent work.
- ROCm and AMD Instinct GPU experience is strongly preferred; deep CUDA ecosystem experience with cross-platform ability is also valued.
- Open-source contributions to AI or machine-learning infrastructure projects are a plus.
- Bachelor's or master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience.
Benefits
- AMD benefits are described in its benefits-at-a-glance materials.
Tech Stack
Categories
Forward Deployed
About AMD
AMD designs and sells CPUs, GPUs, and adaptive/embedded computing products for PCs, data centers, gaming, and edge devices. Its portfolio includes Ryzen and EPYC processors, Radeon and Instinct graphics, and adaptive SoCs from its Xilinx acquisition, sold to OEMs, cloud providers, and device makers. Founded in 1969 and headquartered in Santa Clara, it is a public company on NASDAQ and supplies semi-custom chips for major game consoles.