Meta
Research Scientist Intern, Audio, Numerical Acoustics (PhD)
Redmond, WA · Internship · Posted Oct 14, 2026
- Experience
- 1+ years in one or more specified technical areas; internship is 12–24 weeks.
- Education
- Currently has, or is in the process of obtaining, a PhD degree in a specified or related STEM field.
- Visa sponsorship
- Must obtain work authorization in the country of employment by the time of hire and maintain it during employment.
About the job
Develops and evaluates numerical acoustics algorithms and audio technologies for Reality Labs research, integrating work with engineering teams and communicating findings.
What you'll do
- Design, implement, integrate, and optimize algorithms for audio research problems.
- Design and conduct experiments to evaluate audio technologies.
- Collaborate across research and engineering teams and communicate research progress and results.
What they ask for
- Currently pursuing or obtaining a PhD in Acoustics, Computer Science, Mechanical Engineering, Electrical Engineering, or a related STEM field.
- 1+ years of experience in a listed area such as wave phenomena, acoustic simulation, numerical modeling, applied numerical mathematics, physics-based modeling, or AI/deep learning applied to numerical methods.
- Coding experience in one or more listed languages, including C/C++ (≥ v.11), CUDA C/C++, MPI, OpenMP, Python, or MATLAB.
- Expertise in at least one listed research area, such as numerical acoustics simulation, spatial rendering, vibroacoustics, model verification and validation, or machine-learning-enhanced numerical modeling.
Nice to have
- Accepted papers in relevant conferences or journals; experience with psychoacoustics, deep learning, or DSP fundamentals.
- Experience with parallel computing, algorithm design, data structures, large-scale compute systems, and physical modeling of waves and acoustics.
Pay
$7,650/month to $12,667/month (base salary)
Tools
C/C++ (≥ v.11), CUDA C/C++, MPI, OpenMP, Python, MATLAB
AI focus
May involve applying AI/deep learning to numerical methods or augmenting numerical modeling with machine learning or deep learning.
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