# Staff Machine Learning Scientist at Freenome

- Company: Freenome
- What the company does: 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.
- Company website: https://www.freenome.com
- Type: Startups (AI role)
- Level: Senior
- Location: Remote
- Work setup: Remote
- Pay: $200K to $284K base salary per year (USD)
- Posted: 2026-07-16
- Apply by: 2026-10-08
- Apply: https://job-boards.greenhouse.io/freenome/jobs/8627491002
- Page: https://www.1752.vc/careers/jobs/freenome-staff-machine-learning-scientist/

## 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

Tags: Computational Science