Meta
Research Intern, Audio Hardware (PhD)
Redmond, WA · Internship · Posted Oct 21, 2026
- Experience
- 2+ years of independent research; 2+ years with computer-aided design, acoustic simulation tools, or programming languages such as MATLAB or Python.
- Education
- Currently has, or is in the process of obtaining, a PhD in Acoustics, Computer Science, Mechanical Engineering, Electrical Engineering, or a related STEM field.
- Visa sponsorship
- Must obtain work authorization in the country of employment at the time of hire and maintain it during employment.
About the job
Develop and evaluate wearable audio and biosignal hardware and real-time processing technologies for Reality Labs Research, including prototypes, human-participant data collection, and proof-of-concept demonstrations.
What you'll do
- Design and prototype wearable sensing hardware and low-power architectures for on-body biosignal acquisition.
- Run wearable data-collection studies and assess signal quality, repeatability, and artifact robustness.
- Develop real-time sensor-processing algorithms and present demonstrations, research plans, progress, and results to researchers and cross-functional partners.
What they ask for
- Currently pursuing or holding a PhD in Acoustics, Computer Science, Mechanical Engineering, Electrical Engineering, or a related STEM field.
- 2+ years of independent research experience, evidenced by publications and/or conference presentations.
- 2+ years of experience with computer-aided design, acoustic simulation tools, or programming languages such as MATLAB or Python.
- Must obtain and maintain work authorization in the country of employment; intends to return to a full-time degree program after the internship.
Nice to have
- Track record of significant results, such as grants, fellowships, patents, or first-authored publications at leading journals or conferences.
- Experience working and communicating cross-functionally in a team environment.
Pay
$7,313/month to $12,667/month (currency not stated; compensation type not stated)
Tools
EEG, EMG, EOG, ECG, PPG, EDA, MATLAB, Python
AI focus
Conventional and machine-learning signal processing for physiological data, including classification, regression, event detection, temporal modeling, self-supervised and transfer learning, sensor fusion, and on-device/TinyML inference.
Model-written summary · the original posting is authoritative