Senior AI Platform Development & Validation Engineer

Advanced Micro Devices, Inc•Secaucus, NJ

22d•Onsite

About The Position

This role focuses on leading system-level, rack-level, and cluster-scale validation for next-generation AI infrastructure and accelerated computing platforms. You will define validation strategies, drive technical direction, and lead complex validation initiatives spanning hardware, firmware, software, networking, and infrastructure domains. You will develop and enhance automated validation frameworks, telemetry-driven analysis pipelines, and large-scale test infrastructure used to qualify AMD AI platforms from initial bring-up through customer deployment readiness. The role requires deep technical expertise in hardware-software interactions, strong analytical and debugging skills, and the ability to influence technical decisions across multiple engineering organizations. Working closely with architecture, silicon, platform engineering, firmware, software, manufacturing, and deployment teams, you will identify quality risks, drive root-cause analysis, establish scalable validation methodologies, and ensure product quality, reliability, and deployment readiness at scale.

Requirements

Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or a related technical discipline.
Technical leader with extensive experience validating complex server, rack-scale, or distributed computing environments.
Strong debugging skills, sound technical judgment, and a deep understanding of hardware-software interactions across modern server platforms.
Proactively identify technical risks, establish effective validation strategies, and drive resolution of complex platform issues.
Experience validating large-scale AI, cloud, HPC, server, rack-scale, or data center platforms.
Strong background in hardware-software integration, platform validation, system bring-up, infrastructure qualification, and deployment readiness activities.
Experience developing software and automation using Python, Linux, and modern test orchestration frameworks.
Proven ability to investigate and resolve complex issues spanning hardware, firmware, operating systems, networking, storage, platform management, and distributed infrastructure environments.
Experience leveraging telemetry, observability, and large-scale data analysis to improve validation coverage, root-cause analysis, and engineering decision-making.
Demonstrated ability to lead cross-functional technical initiatives and influence engineering decisions across organizational boundaries.
Strong verbal and written communication skills with the ability to communicate technical findings, quality risks, recommendations, and readiness assessments to engineers, technicians, program managers, and senior leadership.

Nice To Haves

Familiarity with server and rack technologies including: AMD EPYC processors and Instinct accelerators
GPU management and observability frameworks
PCIe architecture and AER diagnostics
BIOS/UEFI and firmware validation
BMC technologies, IPMI, Redfish, and OpenBMC
InfiniBand, Ethernet, RoCE, and scale-up interconnects
Storage technologies and NVMe validation
Cluster management and distributed computing environments
Experience supporting manufacturing validation, ODM/OEM integration, deployment qualification, customer readiness, or hyperscale infrastructure programs.

Responsibilities

Lead system-level, rack-level, and cluster-scale validation strategy for AI server and accelerated computing platforms.
Define and drive comprehensive validation plans that represent real-world customer deployment scenarios across scale-up and scale-out infrastructures.
Design, develop, and maintain automated validation frameworks, orchestration systems, telemetry pipelines, and data-driven workflows used to qualify AI platforms from initial bring-up through platform lifecycle.
Develop validation methodologies spanning CPUs, GPUs, memory subsystems, PCIe fabrics, platform firmware, BIOS/UEFI, BMCs, networking, storage, operating systems, rack management infrastructure, and cluster-level software components.
Execute and analyze large-scale validation workloads across lab, manufacturing, and pre-production environments to evaluate platform reliability, stability, scalability, performance, and operational readiness.
Lead investigation and root-cause analysis of complex hardware, firmware, software, networking, and infrastructure issues using structured debugging methodologies and telemetry-driven analysis.
Develop tooling and automation to collect, aggregate, and analyze health and performance telemetry from technologies such as Redfish, IPMI, OpenBMC, PCIe AER, SEL/SDR logs, operating system diagnostics, GPU telemetry, and platform management interfaces.
Drive technical decision-making within the validation organization and influence cross-functional teams to resolve product quality risks and readiness concerns.
Establish validation readiness criteria, release qualification requirements, coverage metrics, and quality gates that improve execution consistency and early risk identification.
Partner closely with architecture, silicon, firmware, software, systems engineering, manufacturing, deployment, and support organizations to continually improve product quality, reliability, and validation effectiveness.
Own and evolve validation content, test procedures, troubleshooting guides, release documentation, and engineering standards used to support repeatable execution across validation environments.
Work directly with technicians, validation engineers, and operational teams executing your test content, providing technical guidance, reviewing findings, driving procedural consistency, and improving test development practices across programs.
Mentor engineers and provide technical leadership that improves debugging effectiveness, automation capabilities, validation methodologies, and overall engineering excellence.
Communicate validation status, readiness assessments, technical recommendations, quality metrics, and key risks to engineering leadership and stakeholders.
Drive continuous improvement of validation frameworks, automation infrastructure, tooling, documentation, and development processes to increase organizational efficiency, test coverage, and product quality.