Currently has, or is in the process of obtaining, a PhD in machine learning, computer science, hearing science, or auditory or cognitive neuroscience, or a related field 2+ years of experience with Python or a similar language for data analysis and modeling 2+ years of experience in statistical modeling and/or machine learning at the doctoral level, including time-series or sequential data Research experience with one or more of the following: multimodal behavioral data (e.g., eye tracking, motion, physiological, or speech signals), speech perception, auditory attention, or conversational communication Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Proven track record of results as demonstrated by 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 data from wearable or egocentric sensors (e.g., eye tracking, IMU, head-worn microphones), especially in naturalistic or conversational settings Experience with hearing-impaired populations, hearing aids, or cochlear implants Experience modeling under 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 the completion of the internship/co-opMeta builds technologies that help people connect, find communities, and grow businesses. Using multimodal, egocentric data from wearable devices (eye and head movement, speech and vocal behavior, gesture, and the acoustic scene), the intern will build models that infer listening and communication states such as auditory attention, listening effort, and conversational engagement.