Reality Labs at Meta is seeking Research Scientists with experience in prototype machine learning and signal processing research to advance our pioneering work in neuromotor interfaces, which has grown out of the acquisition of CTRL-labs. We're building a practical interface drawing on the rich neuromotor signals that can be measured non-invasively via surface electromyography (EMG) with single motor neuron resolution. This technology could become one of the main pillars for interaction with virtual and augmented worlds. We are a multi-disciplinary team of researchers investigating the nature of human neuromotor signals, developing novel signal processing and machine learning methods to infer a user's intent, and creating novel interaction techniques and user experiences. Help us unleash human potential by removing the bottlenecks between user intent and action. The role is to build, analyze and improve peripheral neural interfaces based on surface EMG technology.

Research Scientist, CTRL Labs Responsibilities

• Build, analyze and improve peripheral neural interfaces based on surface EMG.


• Analyze and develop metrics for deep learning models and experimental datasets


• Design methods, tools and infrastructure to analyze and leverage rich multimodal data sets.


• Design experiments, involving human participants, to assess model performance.

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.


• Programming experience in Python and hands-on experience with frameworks such as PyTorch.


• Exposure to architectural patterns of large scale software applications.

Preferred Qualifications

• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at peer-reviewed AI conferences (e.g. NeurIPS, CVPR, ICML, ICLR, ICCV, ACL, and ICASSP).


• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).


• Experience bringing machine learning-based products from research to production.

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