System Failure Analysis Engineer

Meta; Sunnyvale (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Meta seeks 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 VR headsets, AR glasses, and wearables. You will investigate field failures, identify root causes, and drive corrective actions to improve product quality and durability.

You will also build AI-powered analysis tools, data pipelines, and automated diagnostics, partnering with hardware design,

Qualifications

• Bachelor's degree completed before joining Meta.
• 2+ years in hardware failure analysis or reliability engineering for consumer electronics.
• 2+ years programming in Python/C++ for data analysis or tool development.
• Experience building data pipelines and automation scripts.
• Experience interpreting schematics, PCB layouts, and datasheets.
• Experience applying AI/ML to failure prediction or classification.
• Familiarity with HALT/HASS or accelerated life testing.

Responsibilities

• Perform EFFA and OFFA on returned or customer-reported hardware units across Meta's devices.
• Build and deploy AI-powered tools for failure classification, root-cause prediction, and anomaly detection.
• Develop Python/C++ scripts and data pipelines to streamline failure analysis workflows.
• Apply fault isolation techniques to identify failure modes at board, component, and system levels.
• Conduct physical failure analysis using optical microscopy, X-ray, and cross-sectioning.
• Design automated tests and diagnostic systems leveraging AI/ML for failure detection and triage.
• Document findings in structured reports with root cause and corrective actions.
• Collaborate with hardware design and reliability teams to drive design improvements.
• Analyze quality data trends to identify systemic issues and set investigation priorities.

Skills

AI tools

Machine learning

Data analysis

Automation scripts

Root cause analysis

Python

C++

Hardware failure analysis

Communication

Team collaboration

Education

Bachelor's degree in Computer Science/Engineering or related field

Tools

Python

C++

X-ray inspection

Optical microscopy

Boundary scan

Electrical fault isolation

Cross-sectioning

Schematic interpretation

Data pipelines

Job description

About

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.

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

Minimum Qualifications

• 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 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