Research Scientist Intern, AI-Driven Neural Interfaces (PhD)

Meta•Menlo Park, CA

8d•$7,650 - $12,667

About The Position

Reality Labs at Meta seeks PhD research interns to advance neural interfaces developed from work originating with the acquisition of CTRL-labs. The team is building a practical interface based on rich, non-invasive body signals, including surface electromyography (EMG), inertial measurement units (IMU), and photoplethysmography (PPG), with the potential to become a major interaction method for virtual and augmented reality. Interns join a multidisciplinary research team to investigate human neuromotor and other sensor signals, develop signal-processing and machine-learning methods to infer user intent, and help create AI-first interaction techniques and experiences. Internships last 12 to 24 weeks, with start dates available throughout the year.

Requirements

Currently has or is pursuing a PhD in machine learning, computational neuroscience, speech and language technologies, signal processing, electrical engineering, computer science, statistics, physics, or a related field.
Research-oriented implementation skills, including fluency in scientific Python and deep-learning frameworks such as PyTorch, demonstrated through academic or professional research experience or open-source contributions.
Experience in machine learning, deep learning, signal processing, computer vision, probabilistic graphical models, and/or natural language processing.
Must obtain work authorization in the country of employment by the time of hire and maintain it throughout employment.

Nice To Haves

Intent to return to a degree program after the internship.
Experience working with biosignals or wearable sensor data, such as EMG, IMU, PPG, or EEG.
Experience with multi-channel array processing or real-time signal processing.
Experience with sequence modeling of time-series data.
Experience training machine-learning models at scale, including distributed or multi-GPU training on large datasets.
Experience moving machine-learning research into prototypes or products.
Industry software-engineering experience, including prior internships.

Responsibilities

Research and develop neural networks, machine-learning methods, or other computational models using EMG, IMU, and other relevant sensor data to advance neural-interface capabilities.
Design methods, tools, and infrastructure for analyzing and using multimodal datasets.
Adapt machine-learning methods to modern parallel computing environments, such as distributed clusters and GPUs.
Collaborate with engineering and Human-Computer Interaction (HCI) teams to turn research into technology and user experiences.
Use quantitative research methods to define, iterate on, and advance areas of the research agenda.