Machine Learning Research Engineer
Category: Information Technology
Job Location: Remote
Tracking Code: 21420-1
Position Type: Full-Time/Regular
Certilytics, Inc. provides sophisticated predictive analytics solutions to major healthcare organizations by integrating financial, clinical, and behavioral insights.
Machine Learning Research Engineer. As a machine learning research engineer, you’ll be responsible for designing and running experiments to bring the latest deep learning advances from the literature to our products. As part of the data science team, you will be responsible for building models for clinical and financial risk prediction, performing original research, and contributing to a proprietary machine learning library. The ideal candidate will have a strong background in natural language processing and familiarity with the inner workings of RNN’s and transformer networks (BERT, EMLo, GPT-2, ULMFit, etc.). Join a flexible, energetic team in bringing the best of deep learning to healthcare.
- Develop supervised and unsupervised models to drive health and business insight in the healthcare space
- Participate in the development and selection of new data sources and ensuring the quality and reliability of the data
- Communicate, implement, and integrate statistical tools developed into the internal systems for ongoing use
- Measure and communicate the effect of models in production
- All other duties as assigned.
- Must have a Bachelors Degree in Science or related field
- Must have strong analytical/mathematical skills and working knowledge of machine learning
- Fluency in Python
- Experience with deep learning frameworks such as PyTorch, Keras, or Tensorflow
- Masters Degree or Ph.D. in computer science or related field strongly encouraged but not required
- Experience in software development and version control
- Experience with natural language processing
- Publication record or past research experience
- Robust exploratory/experimental skills. You’ll be responsible for designing and evaluating experiments to predict downstream outcomes.
- Ability to implement models from research. Some of our best improvements have come from doing literature surveys and implementing novel techniques from research papers.