Principal Hardware Engineer

Lead development and implementation of end-to-end agentic/automated debug solutions for @scale datacenter fleet health systems. Lead collaboration projects with hardware, firmware and software teams that drive root cause analysis for one flow end to end; sets the rules agents work within; clears handoffs and share of work closed by agents Accountable for successful execution of targeted agentic defect reduction workflows for novel faults that cross SW, blade components, rack and fleet; turns every fix into a reusable lesson. Lead resolution of complex problems based on technical and business understanding. Sets the goals, specs and evidence bars agents build from to minimize errors caught late. Develop world class at scale debug methodologies to solve problems relating to mission critical services and build automation to drive debug efficiency. Accountable for fleet health agent quality and reliability. Runs persona swarms safely; sets runtime limits for agentic support on incident response and recovery time for agent faults. Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 7+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience OR equivalent experience. 10+ years of technical leadership and a record of owning a measured reliability, quality, capacity or recovery outcome at production scale, using reliability engineering methods (failure-mode analysis, failure-rate statistics, screening and qualification) and working across hardware, firmware and software teams. Deep expertise in at least one layer of server or datacenter infrastructure (CPU or SoC architecture, memory subsystem, storage, networking, virtualization, firmware, cloud-native operating systems, or fleet telemetry and data systems), with working fluency in how failures propagate. Proven ability to lead deep dives into hard failures that have no known playbook, in at-scale environments: testing competing hypotheses, preserving evidence before repair, finding root cause across layers, and converting the result into a permanent fix, runbook, test or automated check A record of setting intent and limits for automated or AI-driven systems: defining success criteria and the evidence bar, evaluating results against outcomes, and deciding what may act without prior human approval, with rollback, an audit trail and a named accountable owner. Experience with AI agents in production operations: setting intent and success criteria, evaluating output against outcomes, curating the memory they rely on, governing what each may do, and solving the problems they have not seen. Analytical rigor with large-scale telemetry and data tools: defining each metric once (population, start and end events, denominator), separating measured results from targets, proxies and estimates, and counting distinct machines, not retries. Experience leading without line authority across organizational boundaries: aligning partners on workflow ownership, interfaces and decision rights, and adopting ideas from other teams on evidence. Decision-ready communication for engineers and executives in writing, visuals and live forums, including technical reports that state the conclusion, the evidence, the confidence and what would change the answer. Planning discipline in a fast-changing environment: works in small, reversible steps, adapts as evidence changes, manages time and priorities, and sets a pace of change the team can absorb.