Meta Reality Labs Research is seeking a scholar of language and social interaction, or interpersonal communication, to conduct applied empirical research in complex social and relational contexts. The ideal candidate will address contemporary communication challenges—such as facilitating difficult or high-stakes conversations, managing conflict, providing social support, mediation, or negotiating relationships—across personal, professional, and community settings, both online and offline. As part of our world-class team, you will help build meaningful representations of user state and social standing using data from egocentric machine perception, supporting the next generation of contextual AI wearables, VR, and AR devices. You will collect and analyze rich behavioral datasets to advance our understanding of human experience, leveraging both qualitative and quantitative social science methods. We welcome applicants whose research intersects with or complements areas such as interpersonal communication, language and social interaction, health communication, organizational communication, communication and technology, computational social science, media studies, or information science. Your research program should be empirical, theory-driven, and communication-centered. Our internships are twelve (12) to twenty four (24) weeks long and we have various start dates throughout the year. Some projects may require a minimum of 24 consecutive weeks.
UX Research Scientist Intern, Multimodal Conversation Analyst (PhD) Responsibilities
- Plan and execute cutting-edge research and development to advance the state-of-the-art in machine perception.
- Collaborate with other researchers and engineers across machine perception teams at Meta to develop AI systems.
- Work with the team to help design, setup, and run practical experiments and analyses related to large-scale high quality sensing and machine reasoning for the representation of social and behavioral primitives.
- You’ll own research end-to-end. This means you’ll navigate trade-offs while designing projects, proposing appropriate methodologies, analyzing data, and communicating results to diverse audiences in order to drive impactful decisions.
Minimum Qualifications
- Currently pursuing a PhD in Human-Computer Interaction, Linguistics, Cognitive Science, Computer Science, Psychology, or a related field with a focus on social interaction or pragmatic language analysis
- Demonstrated experience in computer-assisted analysis of language use, including qualitative and quantitative methods (e.g., conversation analysis, discourse analysis, NLP)
- Proficiency in Python and/or R for data analysis, with experience using relevant libraries (e.g., NLTK, spaCy, pandas, scikit-learn)
- Experience working with specialized datasets, such as: Social interaction recordings (audio/video) Interview transcripts Device telemetry logs Sensor streams (e.g., gaze tracking, gesture recognition, physiological data)
- Familiarity with machine perception models that infer user attention, object handling, and interaction patterns from multimodal data
- Ability to design and implement data collection protocols for behavioral and sensor data
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
Preferred Qualifications
- Hands-on experience integrating and analyzing multimodal datasets, including behavioral, linguistic, and device-derived sensor streams
- Experience with self-reported measures of cognitive load, stress, emotional valence, and arousal, and their integration with objective sensor data
- Track record of publishing research in top-tier conferences or journals related to social interaction, language technology, or human behavior analysis
- Experience developing or applying machine learning models to infer social, cognitive, or emotional states from complex, real-world data
- Familiarity with tools and techniques for synchronizing and annotating multimodal data (e.g., ELAN, Praat, custom annotation pipelines)
- Demonstrated ability to work collaboratively in interdisciplinary teams, communicating complex findings to both technical and non-technical audiences
- Intent to return to one’s degree program after completion of the internship
