Research Scientist Intern, Wearable Sensing & Human Motion Analysis

Meta; Redmond; On-site; AUD 49,000 - 78,000; Part time

Job summary

Meta's Reality Labs Research team invites exceptional researchers to join the Surreal Vision group as an internship focused on learning-based motion analysis for swing sports. You will work on egocentric perception, data collection with wearable or motion-capture hardware, and model development using PyTorch in a lab setting.

Ideal candidates are pursuing a PhD in CS/EE/Robotics/Biomechanics, with strong Python and PyTorch skills, experience with sensor data, and a track record of publications

Qualifications

• Pursuing a PhD in CS, EE, Robotics, Biomechanics or related field.
• Strong Python and PyTorch experience.
• Experience with real sensor data and lab-based experiments.
• First-author publications at CVPR/ICCV/ECCV/NeurIPS/ICLR or biomechanics wearables venues.
• Willingness to label data for swing sports and verify feedback.

Responsibilities

• Develop a learned model for motion analysis for swing sports.
• Contribute to egocentric data collection using the team's simulator and wearable sensors; assist with model development.
• Train and evaluate models in PyTorch; explore time-series models (temporal CNNs, transformers, HMM/CTC).
• Aim for generalization across players/equipment and provide interpretable feedback outputs.
• Assist with data collection and experiments involving human participants in lab settings.

Skills

Python

Time-series modeling

Motion analysis

Data collection

Research collaboration

Education

PhD candidate in CS/EE/Robotics/Biomechanics

Tools

PyTorch

Job description

The Meta Reality Labs Research Team brings together a world-class team of researchers, developers, and engineers to create the future of AR and VR. The Surreal Vision group at RL Research is seeking exceptional Research Scientists to research and help build the egocentric machine perception functionalities and digital twins of 3D scenes that will underpin future VR and contextual-AI-enabled AR devices.

This internship focuses on developing a learned model for motion analysis for swing sports as an alternative to heuristic, threshold-based methods. Research goals include: improving analysis accuracy, generalization across players and equipment, and generating interpretable feedback outputs. The intern will contribute to egocentric data collection using the team's simulator research setup, in addition to model development.

Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year. Some projects may require a minimum of 24 consecutive weeks.

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

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

Experience working with real sensor data, not just curated benchmarks

Comfortable running hands‑on hardware experiments with human participants in a lab setting

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

Experience with surface EMG: filtering, rectification, envelope extraction, electrode‑shift and cross‑session variability

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

Background in sports biomechanics or motion analysis

self‑supervised or few‑shot learning for personalization

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

Plays swing sports - genuinely helpful for labeling and sanity‑checking feedback

Intent to return to a degree‑program after the completion of the internship