Technical Program Manager- AI Rack Scale Validation and Debug

Advanced Micro Devices, Inc•Austin, TX

About The Position

AMD is seeking a Technical Program Manager to lead highly visible AI infrastructure programs focused on GPU platforms, rack-level solutions, and AI cluster validation. The role works across hardware, firmware, networking, systems, and validation teams to drive AI platforms from early integration and bring-up through rack- and cluster-scale readiness. It encompasses GPU and server integration, rack bring-up, scale testing, system debug, failure analysis, and deployment readiness for hyperscale and enterprise environments. The successful candidate will be a hands-on, technically curious program leader who can navigate complex engineering discussions while managing program priorities, risks, dependencies, and business outcomes. The role requires bringing cross-functional teams together, communicating technical issues to engineers and senior leaders, and identifying opportunities to improve engineering workflows through automation, Agentic AI, and AI-driven operational tools.

Requirements

Strong program management skills, including schedule management, dependency tracking, risk management, executive reporting, and cross-functional coordination.
Strong written and verbal communication skills, including the ability to translate complex engineering issues into clear program status, risks, and business impact.

Nice To Haves

Experience leading complex hardware, systems, AI infrastructure, or datacenter programs across bring-up, validation, and deployment.
Technical understanding of GPU-based AI systems, server platforms, rack architectures, and datacenter infrastructure.
Experience with GPU cluster scale testing, system stress testing, performance validation, or large-scale systems validation.
Familiarity with rack-level bring-up and system dependencies including power, cooling, networking, cabling, and infrastructure readiness.
Experience working through hardware and firmware debug cycles in pre-production, validation, deployment, or customer-facing environments.
Experience coordinating cross-functional engineering teams and driving technical issues to closure without direct authority.
Experience with fleet-scale validation or deployment of AI, cloud, HPC, or hyperscale infrastructure.
Understanding of infrastructure observability, telemetry, log analytics, failure analysis, and incident-management practices.
Familiarity with Agentic AI, AIOps, or AI-enabled automation for engineering, validation, debug, or operational workflows.
Experience with tools such as Jira, Confluence, dashboards, Excel, and PowerPoint.
Bachelor’s or master’s degree in Systems Engineering, Electrical Engineering, Computer Science, or a related engineering discipline is preferred.
PMP, Scrum Master, or equivalent program management training or certification is desired.

Responsibilities

Lead end-to-end program planning and execution for GPU-based AI infrastructure, including server integration, rack bring-up, cluster-scale validation, and deployment readiness.
Develop and maintain program schedules, dependency maps, resource forecasts, risk and issue tracking, readiness criteria, and program dashboards.
Drive alignment across GPU, CPU, firmware, BIOS/BMC, systems, networking, lab infrastructure, validation, and other engineering teams.
Coordinate multi-node and multi-rack scale testing, including test strategy, scheduling, coverage tracking, and readiness gates.
Drive rack-level solution execution across compute trays, switch trays, cabling, power, cooling, networking, and management infrastructure.
Establish entry and exit criteria for bring-up, EVT, DVT, scale validation, and deployment phases.
Coordinate platform and rack-level debug activities, helping teams triage issues, identify root causes, assign ownership, and drive issues to closure.
Track high-impact GPU, HSIO, firmware, rack, networking, and system failures through resolution.
Lead incident response for critical validation and deployment issues, including cross-functional debug, escalation management, and executive communications.
Establish and track operational metrics such as MTTD, MTTM, MTTR, incident recurrence, and fleet health.
Drive post-incident reviews, root-cause analysis, corrective actions, and preventive measures to improve reliability and deployment readiness.
Identify opportunities to apply Agentic AI and AIOps to engineering workflows, including automated triage, log analysis, root-cause identification, knowledge retrieval, test orchestration, and incident management.
Partner with data and engineering teams to integrate AI-powered operational tools and establish measurable success metrics for automation initiatives.
Provide clear, data-driven program updates to engineering teams and senior leadership while proactively escalating risks, dependencies, and critical tradeoffs.