CVP of Applied AI FDE in San Jose, California | Advanced Micro Devices, Inc

Amd

Quick summary

Work type
On-site
Location
San Jose, CA
Salary
$295,600–$295,600 / yr
Posted
154 days ago
Closes
Feb 26, 2027
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $195k
This role $296k
$142k most similar roles pay here $312k

This role pays more than 96% of similar roles. Most pay $160,000–$230,518 — the shaded band above. At the midpoint, this role pays about $296k versus about $195k 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 64 open roles on FindRole.

Listed pay typically runs $190,000–$190,000 across 64 roles with salary data.

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At a glance

TL;DR · CVP of Applied AI FDE in San Jose, California | Advanced Micro Devices, Inc

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As a Senior FDE Engagement Lead in the AI Solutions team, you will build and scale a world-class Full-Deployment Engineering (FDE) organization by combining ML Generalists, Low-Level Kernel Optimizers, and Solutions Architects to cover the full customer deployment lifecycle. Your primary responsibilities include overseeing technical onboarding of massive GPU clusters, driving industry-leading Customer GPU Utilization across thousands of GPUs, optimizing open-sourced models for specific hardware topologies, acting as a technical authority in large deals, and channeling field intelligence back to Product Engineering. Preferred skills encompass years of technical leadership experience, understanding the stack from metal up, commercial acumen, crisis management expertise, and proficiency with AI frameworks like PyTorch and TensorFlow, distributed computing tools such as Kubernetes, GPU ecosystem knowledge including NVIDIA drivers and CUDA profiling, and advanced LLM deployment techniques.

Skills

PyTorch TensorFlow Kubernetes Docker Slurm Ray NVIDIA CUDA Nsight Systems Triton Inference Server vLLM TensorRT-LLM LoRA QLoRA RAG pipelines CI/CD Prometheus Grafana AWS Azure Google Cloud

What you'll do

  • Oversee technical onboarding of massive GPU clusters for optimal performance.
  • Drive industry-leading customer GPU utilization across thousands of GPUs.
  • Optimize open-sourced and proprietary models for specific hardware topology.
  • Serve as the technical authority on large deals, validating architecture with executives.
  • Channel field intelligence to Product Engineering to address customer challenges.
  • Lead through high-stakes crises, managing executive communication and root cause resolution.

What we're looking for

  • Proven track record leading high-impact technical teams in high-stakes environments.
  • Deep understanding of AI frameworks (PyTorch, JAX, TensorFlow) and distributed computing tools.
  • Expertise in GPU ecosystem technologies including NVIDIA drivers and CUDA profiling.
  • Experience with advanced LLM deployment techniques such as fine-tuning and RAG pipelines.
  • Ability to translate customer challenges into prioritized engineering roadmaps.
  • Commercial acumen with knowledge of ARR, churn, margin, and their impact on business outcomes.

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