AI/ML Scientist/Developer

Frederickmid

EngineeringData ScienceHealthcare

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Quick Summary

Overview

(ID: 2025-0402) Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics,

Technical Tools

EngineeringData ScienceHealthcare

(ID: 2025-0402)

What We Offer

~3 min read

✓100% Medical, Dental & Vision Coverage for Employees

✓Paid Time Off and Paid Holidays

✓401K match up to 5%

✓Educational Benefits for Career Growth

✓Employee Referral Bonus

✓Flexible Spending Accounts: Healthcare (FSA)

✓Parking Reimbursement Account (PRK)

✓Dependent Care Assistant Program (DCAP)

✓Transportation Reimbursement Account (TRN)

✓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.

✓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.

✓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.