THE PERSON
You are a hands-on, technically curious program leader who thrives in complex, fast-moving engineering environments. You are comfortable diving into technical discussions while also maintaining a clear view of program priorities, risks, dependencies, and business outcomes.
You bring urgency, ownership, and structure to ambiguous problems and are skilled at bringing cross-functional teams together to drive decisions and execution. You can communicate effectively with engineers working through detailed system issues as well as senior leaders who need concise, data-driven program updates. You are also excited about finding new ways to improve engineering workflows through automation, Agentic AI, and AI-driven operational tools.
KEY 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 clear entry and exit criteria for bring-up, EVT, DVT, scale validation, and deployment phases.
Lead coordination of platform and rack-level debug activities, helping teams rapidly triage issues, identify root causes, establish 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 efforts, escalation management, and executive communications.
Establish and track operational metrics such as Mean Time to Detect (MTTD), Mean Time to Mitigate (MTTM), Mean Time to Resolution (MTTR), incident recurrence, and fleet health.
Drive post-incident reviews, root-cause analysis, corrective actions, and preventive measures to improve system 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.
PREFERRED EXPERIENCE
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.
Strong program management skills, including schedule management, dependency tracking, risk management, executive reporting, and cross-functional coordination.
Experience with tools such as Jira, Confluence, dashboards, Excel, and PowerPoint.
Strong written and verbal communication skills, including the ability to translate complex engineering issues into clear program status, risks, and business impact.
ACADEMIC CREDENTIALS
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.
LOCATION: Austin, TX
This role is not eligible for visa sponsorship.
#LI-CS1
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
THE ROLE
We are seeking a Technical Program Manager to lead highly visible AI infrastructure programs focused on GPU platforms, rack-level solutions, and AI cluster validation. In this role, you will work across hardware, firmware, networking, systems, and validation teams to drive AI platforms from early system integration and bring-up through rack and cluster-scale readiness.
You will have the opportunity to work on complex AI infrastructure challenges spanning GPU and server integration, rack bring-up, scale testing, system debug, failure analysis, and deployment readiness. You will partner closely with engineering teams and senior leaders to turn complex technical dependencies into clear execution plans and help deliver reliable, scalable AI solutions for hyperscale and enterprise environments.
This is an opportunity to operate at the center of next-generation AI infrastructure development while helping teams improve how systems are validated, debugged, and deployed at scale.
THE PERSON
You are a hands-on, technically curious program leader who thrives in complex, fast-moving engineering environments. You are comfortable diving into technical discussions while also maintaining a clear view of program priorities, risks, dependencies, and business outcomes.
You bring urgency, ownership, and structure to ambiguous problems and are skilled at bringing cross-functional teams together to drive decisions and execution. You can communicate effectively with engineers working through detailed system issues as well as senior leaders who need concise, data-driven program updates. You are also excited about finding new ways to improve engineering workflows through automation, Agentic AI, and AI-driven operational tools.
KEY 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 clear entry and exit criteria for bring-up, EVT, DVT, scale validation, and deployment phases.
Lead coordination of platform and rack-level debug activities, helping teams rapidly triage issues, identify root causes, establish 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 efforts, escalation management, and executive communications.
Establish and track operational metrics such as Mean Time to Detect (MTTD), Mean Time to Mitigate (MTTM), Mean Time to Resolution (MTTR), incident recurrence, and fleet health.
Drive post-incident reviews, root-cause analysis, corrective actions, and preventive measures to improve system 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.
PREFERRED EXPERIENCE
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.
Strong program management skills, including schedule management, dependency tracking, risk management, executive reporting, and cross-functional coordination.
Experience with tools such as Jira, Confluence, dashboards, Excel, and PowerPoint.
Strong written and verbal communication skills, including the ability to translate complex engineering issues into clear program status, risks, and business impact.
ACADEMIC CREDENTIALS
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.
LOCATION: Austin, TX
This role is not eligible for visa sponsorship.
#LI-CS1
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
