AI/ML Scientist-Developer for Organoids (On-Site Bethesda)

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

Benefits offered by this job

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 (

Job summary

Axle in Bethesda, MD, is seeking an AI/ML Scientist/Developer to join the NIH-supported Standardized Organoid Model Center on-site in Bethesda. You will develop computational models to predict organoid growth and optimize differentiation protocols, contributing to standardized, reproducible organoid systems.

The role requires a PhD in a related field and strong Python/R skills with TensorFlow, PyTorch, or scikit-learn.

Qualifications

• PhD in CS/engineering/math with AI/ML model development experience.
• Strong Python/R skills and ML frameworks (TF, PyTorch, scikit-learn).
• Experience applying computational methods in biological laboratory settings.

Responsibilities

• Design and implement ML models predicting organoid outcomes from protocols and data.
• Develop in silico models simulating organoid growth dynamics.
• Create feedback loops between experiments and computation to improve standardization.
• Collaborate with experimental teams to validate models and with data scientists to integrate multi-modal datasets.

Skills

Python

R

TensorFlow

PyTorch

scikit-learn

AI/ML model development

Education

PhD in CS/engineering/math

Tools

Docker

Kubernetes

Cloud platforms

Job description

Axle in Bethesda, MD, is seeking an AI/ML Scientist/Developer to join the NIH-supported Standardized Organoid Model Center on-site in Bethesda. You will develop computational models to predict organoid growth and optimize differentiation protocols, contributing to standardized, reproducible organoid systems.

The role requires a PhD in a related field and strong Python/R skills with TensorFlow, PyTorch, or scikit-learn.