Meta is looking for interns to advance cutting edge technology at the nexus of audio and health. As a Health Tech Hearing Research Scientist Intern, you will conduct research and deploy those findings to improve the well-being of people worldwide. You will help design and implement new techniques that combine machine learning with classical DSP algorithms to leverage the unique computational resources across our suite of products. This work will involve advancing the state of the art in edge computing, acoustic simulation and training systems, perceptual loss functions, and acoustic machine learning performance.

Research Scientist Intern, Health Tech Hearing Sciences (PhD) Responsibilities:

• Improve our spatial audio datasets both in simulation with ambisonics and in practice with data collected from real-world scenarios.
• Prototype miniature machine learning architectures that can run on cutting edge hardware.
• Use quantitative methods to define, implement, and evaluate key ideas in our team's research agenda.
• Collaborate cross-functionality with firmware engineers and product leads to make sure your work has impact.

• Currently has, or is in the process of obtaining, a PhD in one or more of the following fields: machine learning, speech and language processing, signal processing, electrical engineering, computer science, statistics, physics, or related fields.
• Research-oriented implementation skills, including fluency in scientific Python and PyTorch/Tensorflow, as evidenced by academic or professional research experience or contributions to open source projects.
• Experience in machine learning, signal processing, spatial audio, and/or acoustics.
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

• Strong understanding of classical signal processing techniques.
• Fluency in C/C++.
• Passion for, or prior experience, in edge compute and audio processing.
• Experience training and evaluating machine learning models on distributed networks, e.g., Google Compute Engine or AWS Sagemaker.
• Intent to return to degree program after completion of the internship.
• Experience working and communicating cross functionally in a team environment
• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as IWAENC, ICASSP, NeurIPS, or ICML.