Research Scientist Intern, Multimodal Behavioral Modeling, Hearing Science (PhD)

Meta•Redmond, WA

2d•$7,650 - $12,667

About The Position

Meta Reality Labs Research Audio seeks a PhD intern to model how people listen and communicate in real-world settings. Using multimodal, egocentric data from wearable devices—including eye and head movement, speech and vocal behavior, gestures, and acoustic scenes—the intern will build models to infer states such as auditory attention, listening effort, and conversational engagement. Because ground truth for these states is often sparse or ambiguous, the work will focus on methods that can make useful inferences and inform hearing assistance and other audio technologies that adapt to listeners. The role is intended for candidates with a background in machine learning, computer science, hearing science, or auditory or cognitive neuroscience, and an interest in working across those fields. Projects will be designed with successful applicants to align their skills and experience with current research needs. Internships last 12 to 24 weeks, with various start dates throughout the year.

Requirements

Currently has or is in the process of obtaining a PhD in machine learning, computer science, hearing science, auditory or cognitive neuroscience, or a related field.
At least 2 years of experience using Python or a similar language for data analysis and modeling.
At least 2 years of doctoral-level experience in statistical modeling and/or machine learning, including time-series or sequential data.
Research experience in one or more of the following: multimodal behavioral data (such as eye tracking, motion, physiological, or speech signals), speech perception, auditory attention, or conversational communication.
Must obtain work authorization in the country of employment by the time of hire and maintain ongoing work authorization during employment.

Nice To Haves

A record of results demonstrated through grants, fellowships, patents, or first-authored publications at venues such as ARO, ASA/JASA, Ear and Hearing, Trends in Hearing, JSLHR, ICASSP, Interspeech, ICMI, ETRA, CHI, NeurIPS, or similar.
Experience with deep learning frameworks such as PyTorch, including multimodal or sequence models.
Experience with wearable or egocentric sensor data, such as eye tracking, IMUs, or head-worn microphones, especially in naturalistic or conversational settings.
Experience with hearing-impaired populations, hearing aids, or cochlear implants.
Experience modeling with limited data, weak labels, or ambiguous ground truth.
Experience working with large, high-dimensional datasets from multiple sources.
Experience working and communicating cross-functionally in a team environment.
Intent to return to a degree program after completing the internship or co-op.

Responsibilities

Develop machine learning and statistical models to infer listening and communication behavior from multimodal wearable data, with applications to hearing assistance.
Relate model outputs to hearing science measures, such as speech intelligibility, listening effort, and self-reported communication difficulty, to validate what the models capture.
Design data collection strategies, benchmarks, and metrics for models trained with limited or ambiguous ground truth, including data from listeners with and without hearing loss.
Design, run, and analyze human-subjects experiments in laboratory and naturalistic listening environments.
Collaborate with researchers and engineers across hearing science, audio signal processing, machine learning, and perception science.
Communicate the research agenda, progress, and full analyses of results.