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

The Health Technologies team of the Reality Labs is looking for a research intern to help us develop the next generation machine learning models that aim to improve the public health outcomes, at Meta scale. We are seeking candidates who have experience with the following domains: deep learning architecture design, recommendation systems, natural language processing, computer vision, multimodal learning, self-supervised learning. Biomedical background is not required. Our teams at Meta AI offer twelve (12) to twenty-four (24) weeks long internships and we have various start dates throughout the year. Internships are available at our Menlo Park, Seattle, and New York City locations. Please note we are targeting a Fall Internship with start date options of 9/15, 10/13 and 11/10.

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

• Plan and execute cutting-edge research on developing novel machine learning models that can benefit public health
• Conduct SOTA 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

• Currently has, or is in the process of obtaining, a PhD in computer science, electrical engineering, machine learning, 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: deep learning architecture design, recommendation systems, 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

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