Applications & FAE
Build hands-on demos and partner content using AMD Instinct GPUs, EPYC CPUs, and the AMD ROCm software stack.
Originally posted 12 November 2026 by the employer.
About the role
As a Technical Marketing Engineer on the Enterprise AI Solutions & Ecosystem team, you will translate AMD Instinct GPUs, AMD EPYC CPUs, AMD Radeon GPUs, and the open AMD ROCm software stack into hands-on demos, workshops, and partner-ready content. You will collaborate across AMD and with OEM, ODM, OSV, and ISV partners to validate and bring AI solutions to market.
What you'll do
- Create and deliver workshops, technical training, labs, and demos for partners, customers, sales teams, and AMD field application engineers.
- Translate enterprise AI capabilities into solution briefs, deployment guides, technical blogs, presentations, videos, FAQs, and launch materials.
- Build and maintain demonstrations across AMD Instinct, EPYC, Radeon, and ROCm platforms, covering LLM inference, RAG, agents, and application-layer solutions.
- Partner with OEM, ODM, OSV, and ISV engineering and marketing teams to validate configurations, sharpen messaging, and support joint go-to-market programs.
- Help partners deploy and optimize AI software on AMD platforms by coordinating hardware access, technical resources, documentation, and engineering support.
- Channel partner and field feedback back to internal AMD teams to improve enablement, documentation, and solution readiness.
What you'll need
- Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, or a related technical field.
- Ability to travel approximately 25% or more for partner engagements, workshops, and industry events.
Nice to have
- Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or Artificial Intelligence.
- Hands-on knowledge of GPU architecture, GPU enablement, and AI/HPC environments.
- Linux administration experience, including drivers, package management, and troubleshooting across enterprise distributions.
- Kubernetes and DevOps experience with containers, Helm, operators, CI/CD, and GPU scheduling.
- Experience deploying or optimizing AI inference, particularly LLM workloads using vLLM, SGLang, Triton Inference Server, or KServe.
- Python and shell scripting skills, plus familiarity with Git, with exposure to ROCm, HIP, CUDA, or the NVIDIA ecosystem.
Skills: ROCm, GPU architecture, LLM inference, demos, Kubernetes
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