Freenome is a private biotech company focused on developing blood tests to detect cancer early and make screening accessible for everyone. Backed by a16z, GV and CRV.
About the role
At Freenome, we are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment.
What they're looking for
- PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics
- 6+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modeling techniques
- Expertise demonstrated by research publications or industry achievements, in driving independent research in applied machine learning, deep learning and complex data modeling
- Practical and theoretical understanding of fundamental ML models like generalized linear models, kernel machines, decision trees and forests, neural networks, boosting and model aggregation
- Practical and theoretical understanding of DL models like large language models or other foundation models
- Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning
More about this role
At Freenome, we are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment.
They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer.
The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or...
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