Axle
AI/ML Scientist/Developer
About this role
Axle seeks an AI/ML Scientist/Developer to join the NIH-funded Standardized Organoid Model Center in Frederick, MD. You'll design computational models to predict and optimize organoid growth protocols, applying machine learning to advance tissue engineering standardization and drive research discoveries.
What you'll do
- Design and implement machine learning models predicting organoid development outcomes from protocol parameters and molecular data
- Develop in silico simulations of organoid growth dynamics to identify optimal conditions for reproducible generation
- Create feedback loops between experimental validation and computational predictions to improve protocol standardization
- Collaborate with experimental teams to design validation experiments
- Integrate multi-modal datasets with data scientists for comprehensive analysis
What they're looking for
- Machine learning model development
- Python or R programming
- Computational modeling and simulation
- Data integration and analysis
- Biomedical informatics
- Cross-functional collaboration
- Protocol optimization
- Scientific communication
Benefits
- 100% medical, dental, and vision coverage
- Paid time off and paid holidays
- 401(k) match up to 5%
- Educational benefits for career growth
- Flexible spending accounts (healthcare, parking, dependent care, transportation)
- Employee referral bonus
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Axle
Axle is a bioscience and IT company that builds data pipelines, identity management systems, and computational platforms supporting biomedical research, clinical data integration, and pharmaceutical innovation. The company is hiring Quality Assurance Engineers, Data Engineers, Computer Programmers, Bioinformatics Engineers, and AI/ML Scientists to develop scalable, secure infrastructure and advanced analytics capabilities for research organizations and NIH-funded initiatives.
View all jobs at AxleLikely interview questions
- Describe your experience developing machine learning models for biological or biomedical applications. What were the key challenges and how did you address them?
- Tell us about a time you built predictive models based on experimental or observational data. How did you validate your models and incorporate feedback to improve them?