Remote Machine Learning Engineer
Kintsugi
Time zones: EST (UTC -5), CST (UTC -6), MST (UTC -7), PST (UTC -8), AKST (UTC -9), HST (UTC -10)
The RoleYou 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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