Developer

Axle•Frederick, MD

84d•$115,000 - $130,000

About The Position

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH). Axle is seeking a AI/ML Scientist/Developer to join our vibrant team at the National Institutes of Health (NIH) supporting the Standardized Organoid Model Center in Frederick, MD. The Standardized Organoid Model Center is an NIH-funded initiative dedicated to advancing organoid research through the development of validated, reproducible, and well-characterized organoid models. The center brings together interdisciplinary teams of researchers to establish standardized protocols, develop quality control measures, and create resources that will benefit the broader organoid research community. The AI/ML Scientist/Developer will develop innovative computational models to predict and optimize organoid growth and differentiation protocols. This position represents a unique opportunity to apply cutting-edge machine learning techniques to advance organoid standardization and contribute to the development of predictive models for tissue engineering applications.

Requirements

Candidates must possess a Master's degree or PhD in computer science, engineering, applied mathematics, or a related field with demonstrated experience in AI/ML model development.
Strong programming skills in Python, R, or similar languages are required, along with experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
Previous experience applying computational methods in biological laboratory settings is necessary, with knowledge of experimental design principles and biological data types.

Nice To Haves

Experience with organoid systems or tissue engineering applications is highly desirable.
Knowledge of differential equations, systems biology modeling, and experience with deep learning approaches for biological applications will be considered strong assets.
Familiarity with cloud computing platforms and containerization technologies is preferred.

Responsibilities

Design and implement machine learning models that predict organoid development outcomes based on protocol parameters, environmental conditions, and molecular characterization data.
Develop in silico models that can simulate organoid growth dynamics and identify optimal conditions for reproducible organoid generation.
Create feedback loops between experimental validation and computational prediction to iteratively improve protocol standardization.
Collaboration with experimental teams to design validation experiments and with data scientists to integrate multi-modal datasets will be essential components of this position.