AI/ML Scientist for Organoid Modeling (On-Site Bethesda)

Axle Labs; Bethesda (MD); On-site; USD 100,000 - 130,000; Full time

Benefits offered by this job

Medical coverage for employees

Paid time off & holidays

401K match up to 5%

Educational benefits

Employee referral bonus

Flexible Spending Accounts (FSAs)

Job summary

Axle Labs, based in Bethesda, MD, seeks an AI/ML Scientist/Developer to advance organoid standardization at NIH. You will build predictive ML models and in silico simulations to optimize organoid growth, working closely with experimental teams on validation.

The role requires a PhD in a related field and strong Python/R skills, with experience in TensorFlow, PyTorch, or scikit-learn. On-site at NIH with a competitive salary and comprehensive benefits.

Qualifications

• PhD in computer science, engineering, applied mathematics or related field with demonstrated AI/ML model development experience.
• Strong programming skills in Python or R and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Experience applying computational methods in biological contexts and understanding experimental design and biological data.

Responsibilities

• Design and implement machine learning models to predict organoid development outcomes.
• Develop in silico models to simulate organoid growth and identify optimal conditions.
• Collaborate with experimental teams to validate predictions and iteratively improve protocols.

Skills

Python/R programming

ML model development

Experiment design basics

Education

PhD in computer science, engineering, or applied mathematics

Tools

TensorFlow

PyTorch

scikit-learn

Job description

Axle Labs, based in Bethesda, MD, seeks an AI/ML Scientist/Developer to advance organoid standardization at NIH. You will build predictive ML models and in silico simulations to optimize organoid growth, working closely with experimental teams on validation.

The role requires a PhD in a related field and strong Python/R skills, with experience in TensorFlow, PyTorch, or scikit-learn. On-site at NIH with a competitive salary and comprehensive benefits.