Machine Learning Engineering Intern
About Kintsugi
Kintsugi Founders Grace Chang and Rima Seiilova-Olson earlier this year closed an $8M seed to set a new standard in access to mental healthcare [On Deck]. Kintsugi unlocks well-being by detecting clinical depression and anxiety from 20-seconds of free-form speech, closing mental health care gaps across risk-bearing health systems, saving time and lives.
Together, we can transform mental health on a global scale. Kintsugi is developing smarter mental healthcare infrastructure using voice biomarkers to streamline access to care. Awarded multiple distinctions for novel AI technology through the National Science Foundation, we are shaping the future of the healthcare industry [Inc.] and creating a new era of equality and improved access to healthcare for everyone [MedCity News].
Kintsugi was recently featured in Google Voice Talks for being at the forefront of mental healthcare innovation in the voice biomarker space. Read more about us and what we’ve been up to in the Kintsugi Newsroom.
About the Team
The team is comprised of PhD, MD, MBA, and MS grads from Harvard, Stanford, MIT, Caltech, UCLA, and Berkeley. Meet members of the team on the Kintsugi Blog.
The Role
In this role, you will be a member of a team that develops and applies cutting edge techniques to detect depression and anxiety. You will be part of the Eng/Infra team and contribute to data and ML infrastructure.
Responsibilities:
- Work collaboratively to build a platform for scientific discovery in this exciting new domain of speech for healthcare
- Provide a solid infrastructure for building and advancing the state of the art for speech processing algorithms to quantify mental health severity
Minimum qualifications:
- An advanced degree in Computer Science or other quantitative field
- Strong programming skills with working knowledge of Python
- Experience with building ETL pipelines
- Experience with big data processing technologies (Spark, Airflow)
- Knowledge of deep learning frameworks (e.g. Pytorch, TensorFlow)
- Experience dealing with real world large-scale datasets
- Experience with MLOps
- Experience working in a Linux environment
- Team player with good communication skills (oral and written)
Preferred Qualifications
- Exposure to industry or academic research
- Signal/Speech Processing experience
- Ph.D. in Computer Science, Speech/Signal Processing or Machine Learning