Kintsugi

Machine Learning Engineer

Job description

About Kintsugi


Kintsugi is on a mission to scale access to mental healthcare for all. We are developing novel voice biomarker software to detect signs of depression and anxiety from short clips of free form speech. Awarded multiple distinctions for AI technology and recently named one of Forbes’ Top 50 AI companies to watch in 2022, Kintsugi helps to close mental health care gaps across risk-bearing health systems, ultimately saving time and lives.


At Kintsugi, we believe that mental health is just as important as physical health. We exist to ensure that everyone who needs mental healthcare has access to the right care at the right time. 


See mental health differently with Kintsugi. 


To learn more: https://www.kintsugihealth.com


About the Team



We’re the wearer of many hats dedicated to helping communities find access to mental health. Our mission is ambitious, and each member of our team has an important role to play in helping us realize it. Meet some of the team members who you will learn and grow alongside here.


Responsibilities

  • Design and implement ML models to predict signs of anxiety and depression from speech in a reproducible fashion 
  • Integrate with our fast paced and highly collaborative engineering and research teams to drive model compute and metric performance improvements
  • Identify, evaluate and implement technologies to track and improve performance and reliability of our ML systems
  • Identify sources of bias in our ML models and implement methods to ensure equitable performance
  • Work with our cloud team to define requirements for production model deployment while balancing compute costs and model performance


Qualifications

  • M.S./Ph.D. in Computer Science or B.S. with 2+ years of experience in building production-grade machine learning models in industry and/or academic research settings
  • Strong programming skills in python with extensive experience with the scientific and deep-learning stack (numpy, pandas, numba, torch, tensorflow, jupyter)
  • A proven track record of building end-to-end neural network models and presenting results to colleagues 
  • Experience optimizing the compute performance of models for production
  • Ambitious team player with strong communication skills (oral and written)
  • Experience implementing and experimenting with cutting-edge ML techniques from the literature


Bonus Qualifications

  • Background in speech processing or audio classification
  • Experience with experiment tracking and reproducibility tools (MLFlow, WandB, DataBricks, etc)
  • Experience working in a cloud environment (GCP, AWS, Azure, etc)
  • Recent publication(s) in peer-reviewed AI journals

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