Deep Learning Expert/Developer (On-site)
$90,267–$103,808 year
On-siteBethesda, Maryland, United States
Job Summary
Develop deep learning models to support clinical decision-making for retinal diseases using retinal images, genomic data, and clinical records. Build algorithms for disease detection, severity classification, and longitudinal progression prediction, then optimize deep learning tools for deployment on Android/iOS/Web-based platforms. Apply deep learning techniques to biomedical images and clinical data for clinical decision-making and prediction. This role focuses on the development of tools for retinal disease detection, severity classification, and disease progression prediction. You will work on the Ripple Effect team, directly impacting the Lister Hill Center's work.
Required Qualifications
- Master's degree
- 5 or more years of experience in image analysis, machine learning, and deep learning
- Proven experience writing manuscripts or papers for journals and conferences
- Extensive experience with medical image processing, data analysis, and classification techniques
- Expertise in developing custom deep learning workflows, including visualizing activations
- Strong understanding of machine learning techniques, including Support Vector Machines (SVM), Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNN)
- Proficiency in Decision Tree algorithms such as Random Forest, XGBoost, and LightGBM
- Experience in statistical analysis of experimental results
- Familiarity with Linux and Windows environments
- Hands-on experience with high-performance GPU clusters and multi-GPU setups for deep learning tasks
- Expertise in programming with Python, Keras, and PyTorch
- Experience in developing Android, iOS, or web applications
- Must be able to work in the U.S. without employer sponsorship
- Must be able to lift 50 lbs
Desired Qualifications
- PhD in computer science, computer engineering, or relevant field
- Intermediate experience with (levels 03+) Microsoft Office productivity software and collaboration tools such as Microsoft Teams and SharePoint
- Intermediate experience with workplace AI tools, including their limitations and risks, and how they can be applied to support project management tasks
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