可穿戴传感与人体运动分析研究科学家实习生

Meta（Meta Platforms）岗位：Research Scientist Intern, Wearable Sensing & Human Motion Analysis Responsibilities

Operate egocentric data collections on our state-of-the-art in-lab simulator - instrument participants with Aria Gen 2 and Neural Band wearables, run capture sessions, and use the simulator's ground-truth as supervision and validation signal for your models.

Design and train models over multimodal egocentric time-series: Aria Gen 2 (SLAM/IMU, eye gaze, RGB) fused with Neural Band surface EMG and wrist IMU.

Tackle the core modeling problems: temporal swing phase segmentation (address → takeaway → impact → follow-through), kinematic/intent estimation from a head-mounted and wrist-worn viewpoint, and swing-quality scoring.

Build an evaluation harness that benchmarks the model against the existing threshold baseline on held-out players - accuracy, phase-boundary error, and cross-subject generalization.

Define the capture and labeling protocol, and handle the realities of small, noisy, personalized datasets (self-supervised pretraining, augmentation, subject-adaptive fine-tuning).

Integrate the winning model back into our prototype and demo it end to end.

Write up results; strong work is a candidate for publication or an internal tech report.

Currently has, or is in the process of obtaining a PhD degree in the domain of Computer Science, Electrical Engineering, Robotics, Biomechanics, or a related field

Working knowledge of sequence/time-series modeling (temporal CNNs, transformers, state-space models, HMM/CTC-style segmentation)

Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Experience designing and executing your own data collections with wearable or motion-capture hardware

Research experience with IMU-based human activity recognition, pose/motion estimation, or wearable biosignals

Familiarity with egocentric perception (Project Aria, Ego4D/Ego-Exo4D, Aria Digital Twin) or multimodal sensor fusion

First-author publications at CVPR/ICCV/ECCV/NeurIPS/ICLR, or in biomechanics and wearables venues

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Individual compensation is determined by skills, qualifications, experience, and location.

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Currently has, or is in the process of obtaining a PhD degree in the domain of Computer Science, Electrical Engineering, Robotics, Biomechanics, or a related field.

Strong Python and hands-on experience training models in PyTorch.