Job description

At Meta, we're building the future of human connection and the technology that enables it, continuously inventing for the next generation of experiences. To move toward advanced machine intelligence, we're enabling AI to go beyond vision and interact with the physical world. As we move closer to a future with intelligent robots and advanced AI models, we're hiring talent across robotics hardware, system software, machine perception, and artificial intelligence. You'll tackle open problems in robotics and AI, with the opportunity to shape new ways people connect around the world. We build complex electromechanical hardware, and the parts that move are the highest-risk elements of any program. This role owns failure analysis for hardware that fails design-verification, qualification, and production testing — from first symptom through root cause to a documented, transferable corrective action. You will work in the lab with hardware in your hands, in the data with logs and telemetry, and across electrical engineering, firmware, mechanical engineering, and quality teams to close the loop on defects before they reach a product. Scope spans actuators and drive systems, precision mechanisms, sensors, and the electronics and firmware that control them. Own end-to-end FA for units failing fixture, DVE, and qualification testing: intake, symptom capture, hypothesis-driven teardown, root cause, disposition, and corrective-action follow-through Build and maintain a failure-mode taxonomy for the hardware in scope and classify incoming failures against it, so root causes transfer between units instead of being re-derived every time Diagnose by signature, not by guesswork — isolate the failing domain (mechanical, electrical, firmware, thermal, process) using periodicity, load-vs-position behavior, electrical waveforms, calibration and configuration data, and environmental correlation Write and maintain analysis tooling (Python) that parses test-station reports and time-series logs to classify failure modes automatically at fleet/population scale Run fleet-level correlation studies to accept or refute candidate root causes, and document refuted leads as clearly as confirmed ones Drive nonconformance (NC) and CAPA workflows: file, triage, and close NCs; present findings at CCB / design review; feed pass-fail criteria improvements back into the test stations Partner with design ME/EE/FW on containment and permanent fixes, and with suppliers on incoming quality and process escapes Perform and interpret physical FA: precision teardown, dimensional/metrology inspection, wear and fracture surface inspection, optical/microscopy, and — where available — CT, SEM/EDX, and cross-sectioning Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 5+ years of hands-on failure analysis, reliability, or test engineering on electromechanical hardware (motors and drives, gearboxes, precision mechanisms, consumer devices, instrumentation, or similar) Demonstrated root-cause methodology: 5-Why, fishbone, fault-tree, DOE, or equivalent, applied to real hardware failures you can talk through in detail Hands-on lab competence: DMM, oscilloscope, power supplies, load/test fixtures, DAQ Experience writing technical FA reports that enable design engineers to act on findings without additional clarification — FA reports that a design engineer can act on without a meeting Familiarity with motor control and firmware concepts: commutation, field-oriented control, current limits, calibration tables, and encoder error sources Working knowledge of BLDC/PMSM motors, gear trains, bearings, brakes, and position encoders Working knowledge of quality systems: 8D, PFMEA, DFMEA, control plans, MSA/Gage R&R Experience taking electromechanical assemblies — robotic joints and actuators, or comparable precision hardware — from prototype through DVT/PVT into production Experience querying and building on large test-data pipelines and dashboards Reliability engineering depth: HALT/HASS, Weibull and life-data analysis, rainflow/duty-cycle load-spectrum analysis, accelerated life testing Experience with metrology and analytical FA labs (CT, SEM/EDX, metallography, tribology)

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aigopython

Platform: meta
Effort: medium

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