Staff Machine Learning Scientist

Freenome; United States; Hybrid; USD 180,000 - 240,000; Full time

Job summary

Freenome seeks a Staff Machine Learning Scientist to lead AI research within the Computational Science group. You will develop models to detect cancer signals from blood, collaborate with biologists and ML engineers, and push the boundaries of ML/DL in a cross-functional environment.

The role emphasizes independence, publication/industry impact, and a commitment to ethical AI development within a hybrid work setting based in California or remote.

Qualifications

• PhD or equivalent in a quantitative field with AI emphasis.
• 6+ years postdoc or post-PhD experience delivering impactful ML/DL results.
• Publications or industry achievements showing independent ML research.
• Strong grounding in ML models: GLMs, kernels, trees, neural nets, boosting.
• Practical and theoretical DL knowledge incl. LLMs/foundation models.

Responsibilities

• Pursue cutting-edge research in AI applied to cancer, genomics, computational biology, immunology, etc.
• Build or fine-tune models to identify biological changes from disease.
• Develop high-accuracy models with robust generalization to new data.
• Apply interpretability techniques to understand signals and potential biological mechanisms.
• Collaborate with ML Engineering to ensure infrastructure supports model training and iteration.
• Maintain a mindful, transparent, and humane approach to work.

Skills

AI fundamentals

ML techniques

DL methods

Interpretability

Cross-functional collaboration

Education

PhD or equivalent (AI emphasis)

Job description

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 remote.

What you’ll do:

• Independently pursue cutting edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.).
• Build new models or fine‑tune existing models to identify biological changes resulting from disease.
• Build models that achieve high accuracy and that generalize robustly to new data.
• Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms.
• Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration.
• Take a mindful, transparent, and humane approach to your work.

Must haves:

• 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