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# AI/ML Scientist/Developer

- **Company:** Exel Inc.
- **Location:** United States
- **Salary:** $100,000.0 - $130,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Simulation, Computer Programming, Python (Programming Language), Machine Learning, Tensorflow, Cloud Platform System, Pytorch, Deep Learning, Containerization, Scikit Learn, Information Technology
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/ai-ml-scientist-developer-axle-8112537

## About the Role

* Candidates must possess a 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., * 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.

Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.

## Description

* The successful candidate will design and implement machine learning models that predict organoid development outcomes based on protocol parameters, environmental conditions, and molecular characterization data.
 * They will develop in silico models that can simulate organoid growth dynamics and identify optimal conditions for reproducible organoid generation.
 * The role involves creating 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.


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