Deep Learning Expert/Developer (On-site)

Ripple Effect•Bethesda, MD

9d•Onsite

About The Position

As a Deep Learning Developer on the Ripple Effect team, you will play a pivotal role in shaping our success in this important role with the NIH! Your work will directly impact the Lister Hill Center's work. This position involves applying deep learning techniques to biomedical images and clinical data for clinical decision-making and prediction. The role focuses on the development of tools for retinal disease detection, severity classification, disease progression prediction, and more. The ideal candidate will have a strong background in deep learning, particularly for medical image analysis, and experience with high-performance computing environments.

Requirements

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 (CNNs).
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.

Nice To Haves

PhD in computer science, computer engineering, or relevant field
Familiarity with Linux and Windows environments.
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.

Responsibilities

Develop deep learning models to support clinical decision-making for retinal diseases using retinal images, genomic data, and clinical records.
Develop algorithms for disease detection, severity classification, and longitudinal progression prediction, focusing on retinal diseases as well as other patient data.
Build and optimize deep learning tools for deployment on Android/iOS/Web-based platforms.
Other related duties as assigned.