Technical Program Manager, AI Rack Scale Validation and Debug

Amd

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Austin, TX
Salary
$162,640–$243,960 / yr
Posted
2 days ago
Freshness
Confirmed live yesterday
Closes
Nov 2, 2027
Nearby
99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $205k
This role $203k
$153k $255k
below market most similar roles pay here above market

This role pays less than 52% of similar roles. Most pay $174,106–$236,625 — the blue band above. At the midpoint, this role pays about $203k versus about $205k 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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View all roles at Amd

At a glance

TL;DR · Technical Program Manager, AI Rack Scale Validation and Debug

Role Posting Log in to save

The Technical Program Manager- AI Rack Scale Validation and Debug leads highly visible AI infrastructure programs focused on GPU platforms, rack-level solutions, and AI cluster validation. Working across hardware, firmware, networking, systems, and validation teams, the role involves driving platforms from early system integration through rack and cluster-scale readiness. Key responsibilities include managing schedules, dependency maps, and risk tracking while coordinating multi-node testing for compute trays, cabling, power, and cooling. The position requires leading platform-level debug activities, tracking GPU and firmware failures, and establishing operational metrics like MTTD and MTTR. Candidates must possess technical knowledge of GPU-based AI systems, server platforms, and datacenter infrastructure. Required skills include program management, cross-functional coordination, and familiarity with Agentic AI, AIOps, Jira, Confluence, and hardware debug cycles.

What does a Technical Program Manager earn?

Median $190350 from 281 postings across 47 companies.

See salary data

Skills

GPU AI Infrastructure Server Integration Rack Architecture Firmware BIOS BMC Networking Cluster Validation root-cause analysis AIOps Agentic AI Jira Confluence Telemetry Log Analytics HPC Systems Engineering

What you'll do

  • Lead end-to-end program planning and execution for GPU-based AI infrastructure, including server integration and cluster-scale validation.
  • Develop and maintain program schedules, dependency maps, resource forecasts, risk tracking, and readiness dashboards.
  • Coordinate multi-node and multi-rack scale testing, including test strategy, scheduling, and coverage tracking.
  • Drive rack-level solution execution across compute trays, switch trays, cabling, power, cooling, and networking infrastructure.
  • Lead platform and rack-level debug activities to triage issues, identify root causes, and drive technical problems to closure.
  • Manage incident response for critical validation issues, including cross-functional debug efforts and executive communications.
  • Establish and track operational metrics such as MTTD, MTTM, and MTTR to monitor fleet health and reliability.
  • Identify and integrate Agentic AI and AIOps tools to automate engineering workflows like log analysis and incident management.

What we're looking for

  • Lead end-to-end program planning and execution for GPU-based AI infrastructure, including server integration and cluster-scale validation.
  • Develop and maintain program schedules, dependency maps, resource forecasts, risk tracking, and readiness dashboards.
  • Coordinate multi-node and multi-rack scale testing, including test strategy, scheduling, and coverage tracking.
  • Lead platform and rack-level debug activities to triage issues, identify root causes, and drive issues to closure.
  • Establish and track operational metrics such as MTTD, MTTM, MTTR, and fleet health.
  • Experience leading complex hardware, systems, AI infrastructure, or datacenter programs across bring-up, validation, and deployment (preferred).
  • Technical understanding of GPU-based AI systems, server platforms, rack architectures, and datacenter infrastructure (preferred).
  • Bachelor’s or master’s degree in Systems Engineering, Electrical Engineering, Computer Science, or a related engineering discipline (preferred).
  • PMP, Scrum Master, or equivalent program management training or certification (preferred).

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