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Mechanical Engineer, Data Systems Hardware

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Summary

Mechanical Engineer on Meta's Reality Labs Data Systems Hardware team, designing head-worn and body-worn data collection prototypes from concept through deployment — integrating sensors (cameras, IMU, audio), illumination, and data aggregation while managing thermal, mass, fit, and comfort constraints for wearable devices.

Reality Labs is seeking an Mechanical Engineer to lead the design of internal data collection hardware. The Data Systems Hardware & Operations team designs, builds, and deploys the devices and systems used to capture the datasets behind Reality Labs products. This work spans development of data collection headsets and wearable prototypes, sensor-integrated form factor mockups, and the necessary infrastructure to integrate these systems into data collection operations. These systems support Codec Avatars, Input, and Health programs across all Reality Labs devices, and enable the algorithm development and research behind those programs. This role owns mechanical architecture and design for data collection devices from concept through deployment to collection sites. Design work centers on head-worn and body-worn prototypes: integrating sensors (cameras, IMU, audio, etc.), illumination, and data aggregation into wearable form factors, holding sensor alignment and repeatable positioning across participants, managing thermal and mass distribution under wear constraints, and meeting fit, comfort, and participant safety requirements across a range of head and body geometries. Prototypes must be durable enough for sustained use by non-engineering operators and serviceable in the field. The role operates across mechanical, optical, electrical, systems, and firmware boundaries, and partners with research, program management, and data collection operations. Full system requirements are often undefined at project start. Research direction and program priorities shift, and requirements are established through prototyping and measurement. Candidates should have experience working in high-ambiguity, fast-paced development environments.

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