Research Scientist, Machine Learning for Wearables

Meta•New York, NY

1d•$122,000 - $181,000

About The Position

Reality Labs at Meta is seeking research scientists with experience in product-focused machine learning and signal processing to advance neuromotor interfaces developed from the acquisition of CTRL-labs. The work focuses on a practical interface that uses non-invasive surface electromyography (EMG) and inertial measurement unit (IMU) signals, with single motor neuron resolution. This technology could become a key interaction method for virtual and augmented reality. The multidisciplinary team studies human neuromotor signals, develops signal-processing and machine-learning methods to infer user intent, and creates new interaction techniques and user experiences. The role aims to improve neuromotor interface speed and accuracy by reducing the latency between user intent and action, and to help transition research into products.

Requirements

Currently has or is pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Statistics, Mathematics, or a relevant technical field.
Currently has or is pursuing a Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience; the degree must be completed before joining Meta.
Programming experience in Python and hands-on experience with frameworks such as PyTorch.
Experience developing machine-learning models at scale.
Experience with architectural patterns of large-scale software applications.
Must obtain work authorization in the country of employment by the time of hire and maintain work authorization during employment.

Nice To Haves

First-author publications at peer-reviewed AI conferences such as NeurIPS, CVPR, ICML, ICLR, ICCV, ACL, or ICASSP.
Experience bringing machine-learning-based products from research to production.
Industry software-engineering experience.

Responsibilities

Research and develop neural networks, machine-learning models, or other computational models using EMG, IMU, PPG, and/or other relevant data.
Design methods, tools, and infrastructure for analyzing and leveraging rich multimodal datasets.
Help transition and deliver research work into a product.
Adapt standard machine-learning methods to modern parallel environments, such as distributed clusters, multicore SMP, and GPUs.