The company is seeking a Machine Learning Engineer to join their algorithm team to develop and implement machine learning algorithms for biomedical time-series data and small datasets.
Requirements
- Deep expertise in Python (including pandas, numpy, scipy, matplotlib/plotly, scikit-learn, and testing frameworks such as pytest or unittest).
- Experience with Matlab and C/C++ desired.
- High degree of comfort with CI platforms, Git, Jira, and Bitbucket.
- Knowledge of signal processing methods is desired.
- Strong, first-principles understanding of probability, classical statistics, linear algebra, optimization, and classical as well as modern machine learning methods combined with keen intuition.
Responsibilities
- Conduct ETL, EDA, and feature engineering.
- Design, prototype, and implement machine learning algorithms and data analysis procedures, with emphasis on biomedical time-series data and small datasets.
- Develop robust, practical methods for classification, anomaly detection, regression, and prediction problems.
- Develop, test, and maintain production-grade python following best practices in version control, testing, and documentation.
- Collaborate across teams to translate algorithmic concepts into rigorously tested real-world embedded and/or cloud implementations.
- Communicate findings and methods clearly to both technical and non-technical stakeholders.
Other
- B.S. or M.S. in Computer Science, Electrical Engineering, or a closely related field.
- Minimum of 5+ years designing, testing, and deploying machine learning algorithms in real-world applications.
- Rock solid written and verbal communication skills for both technical and non-technical audiences.
- Combines systematic, detail-oriented thinking with creativity and the ability to apply the 80/20 rule effectively when quick turnarounds are expected.
- 9/80 Work Week
