You will be part of the Machine Learning (ML) team and contribute to building robust, production-ready models. You will leverage our extensive speech dataset while experimenting with a multitude of  deep-learning architectures to explore state-of-the-art speech analysis methods to solve a variety of classification and regression tasks. Working alongside our cloud engineering team, you will help deploy these models and ensure they stay performant in a wide range of customer-facing applications.

Kintsugi offers a holistic Total Rewards package designed to support our employees in all aspects of their life inside and outside of work. The expected base salary for this position will range from $150K-$210K. Actual compensation may vary from posted base salary depending on your confirmed job-related skills and experience.

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• 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

Minimum Qualifications

• M.S./Ph.D. in Computer Science or equivalent or B.S. with 5+ 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)
• Background in speech processing or audio classification
• 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

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Bonus Qualifications

• 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