The Machine Learning Scientist/Sr Scientist, Federated Benchmarking & Validation Engineering plays an essential role within the TuneLab platform, responsible for identifying, assessing, and implementing cutting-edge algorithmic solutions that leverage diverse datasets while ensuring data privacy and security for our biotech partners
Job Summary
The Machine Learning Scientist/Sr Scientist, Federated Benchmarking & Validation Engineering plays an essential role within the TuneLab platform, responsible for identifying, assessing, and implementing cutting-edge algorithmic solutions that leverage diverse datasets while ensuring data privacy and security for our biotech partners.
This role centers on constructing robust validation frameworks for federated models, creating privacy-preserving test sets across partner datasets, establishing standardized benchmarks against public datasets, and ensuring model reproducibility and generalization in diverse deployment scenarios.
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance).
Matching Summary
The Machine Learning Scientist/Sr Scientist, Federated Benchmarking & Validation Engineering plays an essential role within the TuneLab platform, responsible for identifying, assessing, and implementing cutting-edge algorithmic solutions that leverage diverse datasets while ensuring data privacy and security for our biotech partners.
Salary
$151,500 - $244,200; Bonus eligible; Comprehensive benefits program
Skills & Requirements
Must-have
Federated learning
Privacy-preserving protocols
Model validation frameworks
Drug discovery data
Machine learning models
Nice-to-have
Scientific rigor
Attention to detail
Portfolio mindset
Collaborative approach
Key Requirements
PhD in Computational Biology, Bioinformatics, Cheminformatics, Computer Science, Statistics, or related field
2+ years of experience in biopharmaceutical industry