Research Scientist Intern, Machine Learning, Health Tech (PhD)

Meta - Burlingame, CA

Full-time - Intern

Burlingame, CA

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About the position

The Health Technologies team at Reality Labs is seeking a research intern to contribute to the development of advanced machine learning models aimed at enhancing public health outcomes on a large scale. This internship will involve cutting-edge research in various domains of machine learning, including biomedical engineering, natural language processing, and computer vision, among others. Interns will have the opportunity to collaborate with experienced researchers and engineers, prototype and test AI/ML models, and present findings to stakeholders.

Responsibilities

• Plan and execute cutting-edge research on developing novel machine learning models that can benefit public health.
• Conduct state-of-the-art research while collaborating with other researchers and engineers to develop, prototype, and test different AI/ML models at scale for real-world health applications.
• Present and share results with key stakeholders.
• Troubleshoot, debug, and upgrade existing models, contribute to code documentation and maintenance of the data pipeline.

Requirements

• Currently has, or is in the process of obtaining, a PhD in computer science, electrical engineering, machine learning, biomedical engineering, or related fields.
• Experience with Python and in using deep learning libraries and related frameworks, such as Pytorch, TensorFlow, numpy, pandas, scikit-learn.
• Research skills involving defining problems, exploring solutions, and analyzing and presenting results.
• Deep understanding of at least one of the following areas: machine learning for biomedical engineering, natural language processing, computer vision, multimodal learning, self-supervised learning.
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.

Nice-to-haves

• Experience working and communicating cross-functionally in a team environment.
• 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.
• Proven track record in the design of novel and fieldable machine learning models for health-related applications.
• Experience working with time sequence form sensor data, such as PPG, CKG, IMU, and audio.
• Intent to return to the degree program after the completion of the internship/co-op.

Benefits

• $7,500/month to $11,333/month compensation
• Benefits package