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Posted

✅ 43 applicants

Remote Machine Learning Engineer

Kintsugi

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Full-TimeAll Other RemoteUSA Only
Time zones: EST (UTC -5), CST (UTC -6), MST (UTC -7), PST (UTC -8), AKST (UTC -9), HST (UTC -10)


The Role

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.

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 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)
  • 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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