System Failure Analysis Engineer

Meta•Sunnyvale, CA

7d•$118,000 - $170,000

About The Position

Meta is seeking a System Failure Analysis Engineer to join our Hardware Quality and Reliability Engineering team, focusing on Early Field Failure Analysis (EFFA) and Ongoing Field Failure Analysis (OFFA) for consumer hardware products including virtual reality headsets, augmented reality glasses, and wearable devices. In this role, you will investigate hardware failures from field returns and customer-reported issues, identify root causes, and drive corrective actions that improve product quality and reliability. You will leverage strong software development skills and AI tool-building capabilities to create automated analysis solutions and intelligent diagnostic systems. You will partner closely with hardware design, manufacturing, field support, and reliability engineering teams to ensure Meta's next-generation devices meet the highest standards of performance and durability.

Requirements

Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
2+ years of experience in hardware failure analysis, hardware quality engineering, or reliability engineering for consumer electronics or similar electromechanical systems
2+ years of hands-on experience with failure analysis techniques including electrical fault isolation, optical and electron microscopy, X-ray inspection, or cross-sectioning
2+ years of experience with programming languages such as Python, C++, or similar for data analysis, automation, or tool development
Experience building data analysis pipelines, automation scripts, or diagnostic tools to support engineering workflows
Experience interpreting schematic diagrams, PCB layouts, and component datasheets to support system-level fault isolation
Experience documenting and communicating technical failure analysis findings to cross-functional engineering teams and stakeholders

Nice To Haves

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience developing AI or machine learning models for failure prediction, classification, or anomaly detection
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Familiarity with reliability testing methodologies such as HALT, HASS, or accelerated life testing in the context of consumer hardware qualification
Experience with Early Field Failure Analysis (EFFA) or Ongoing Field Failure Analysis (OFFA) programs for consumer electronics
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience building internal tools, dashboards, or automated systems to improve engineering team productivity
Experience with failure analysis of wearable devices, AR/VR hardware, or compact consumer electronics with complex system-on-chip or flexible circuit assemblies
Experience using statistical quality tools (e.g., Pareto analysis, Weibull analysis, control charts) to identify and prioritize systemic hardware failure trends

Responsibilities

Perform Early Field Failure Analysis (EFFA) and Ongoing Field Failure Analysis (OFFA) on returned or customer-reported hardware units across Meta's consumer device portfolio, including VR headsets, AR glasses, and wearable electronics
Build and deploy AI-powered tools and machine learning models to automate failure classification, root cause prediction, and anomaly detection in field failure data
Develop software scripts, data pipelines, and analysis tools using Python or similar languages to streamline failure analysis workflows and improve diagnostic efficiency
Apply fault isolation techniques such as electrical characterization, boundary scan, and functional diagnostics to identify failure modes at the board, component, and system level
Conduct physical failure analysis using tools such as optical microscopy, scanning electron microscopy, cross-sectioning, and X-ray inspection to determine root cause
Design and implement automated test and diagnostic systems that leverage AI and machine learning to enhance failure detection and triage capabilities
Document failure analysis findings in structured reports, including failure mode classification, root cause determination, and recommended corrective actions
Collaborate with hardware design and reliability engineering teams to translate failure analysis findings into design improvements and process changes
Track and analyze failure trends using quality data systems and custom-built analytical tools to identify systemic issues and prioritize investigation efforts
Communicate failure analysis results and quality insights to engineering leaders and stakeholders across varying levels of technical background