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AMD Logo AMD · IT - Software Development

Technical Program Manager- AI Cluster Validation

United States, Austin Employment contract 162640.00 - 243960.00 USD / Year · Job Posted May 04, 2026
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Job Description

We are seeking a Technical Program Manager to lead execution of AI cluster engineering programs with deep focus on GPU platforms, rack-level solutions, and AI Cluster validation. This role is responsible for driving end-to-end delivery from GPU + server integration through rack bring-up, scale testing, failure analysis, and system debug closure, ensuring platform readiness for hyperscale and enterprise AI deployments. This role operates at the intersection of hardware, firmware, networking, and scale-test execution, and requires strong technical depth combined with disciplined program execution.

Job Responsibility

  • Define, plan, and drive program plans for AI infrastructure systems validation and readiness, including server integration, rack bring-up, and cluster-scale deployment readiness
  • Create and maintain core PM artifacts: schedules, dependency maps, resource forecasts, risk/issue logs, and program dashboards/status reports
  • Identify and drive mitigation plans for issues/risks, including cross-team escalations and corrective actions across multiple engineering areas
  • Drive regular execution reviews with engineering teams and provide concise, data-driven updates to senior leadership
  • Own program execution for GPU-based AI platforms, spanning system bring-up, qualification, scale readiness, and deployment validation across server, rack, and cluster levels
  • Drive alignment across GPU, CPU, firmware, BIOS/BMC, and system teams to ensure readiness for scale testing and customer workloads
  • Track platform issues, and debug dependencies
  • ensure risks are clearly documented, owned, and mitigated
  • Own program planning and execution for multi-node and multi-rack scale testing, including test strategy, scheduling, coverage tracking, and readiness gates
  • Lead end-to-end delivery of rack-level AI solutions, including compute trays, switch trays, cabling, power, cooling, and management infrastructure
  • Ensure rack bring-up plans are executable, resourced, and gated with clear entry/exit criteria across EVT, DVT, and scale phases
  • Drive coordination across lab operations, infrastructure, and engineering teams to unblock rack access, power, networking, and test readiness
  • Partner with scale, performance, and automation teams to ensure workloads, stress tests, and regressions plans are ready before hardware arrives
  • Act as the execution lead for platform debug, coordinating across engineering teams to ensure fast triage, root-cause analysis, and resolution of system-level issues
  • Track high-impact failures (GPU, HSIO, FW, rack, network) through debug forums ensuring clear ownership and closure plans
  • Balance debug depth vs. program timelines, escalating tradeoffs when needed and ensuring leadership has a clear view of risk and impact

Requirements

  • Experience leading complex hardware or AI infrastructure programs with ownership across bring-up, validation, and deployment phases
  • Strong technical understanding of GPU-based AI systems, rack architectures, and datacenter infrastructure
  • Proven ability to manage ambiguity, drive debug execution, and lead cross-functional teams without direct authority
  • Strong written and verbal communication skills, including executive-level status reporting
  • Proficiency with program management and execution tools (Jira, Confluence, dashboards, Excel/PowerPoint)
  • Bachelor's or master's degree in systems, EE, CS, or related engineering discipline
  • PMP, Scrum Master, or equivalent program management training

Nice to have

  • Hands-on experience with GPU cluster scale testing, system stress, or performance validation
  • Familiarity with rack-level bring-up, power/cooling constraints, networking, and failure modes at scale
  • Experience working through hardware/firmware debug cycles in pre-production or customer-facing environments
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