Staff Computational Biologist

Experience training, evaluating, or applying biological sequence models—specifically sequence-to-function models that learn and predict functional biology directly from raw DNA, RNA, or protein sequences.

Experience developing or applying single-cell genomic or transcriptomic models to translate biological heterogeneity into generalizable regulatory or epigenomic signatures.

Experience modeling cell-free DNA (cfDNA) signals specifically for early disease detection and diagnostic applications.

The US target range of our

base salary rate

for new hires is $188,275 - $270,375.

You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.  Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @

freenome.com/job-openings/

for additional company information.

In this role, you will apply your background in computational biology and statistics to drive the creation of new applications and enhance algorithms for identifying molecular signatures of cancer.

You will collaborate closely with machine learning scientists and computational biologists to iteratively advance computational models and assays, playing a key role in bringing products to the clinic.

The role reports to the Manager and Staff Computational Biologist leading genomics modeling within Computational Science.

Serve as a key thought-leader on the Computational Science team, leading the analysis and interpretation of cancer's molecular signatures and staying current with the field.

Guide and contribute to the development of models that characterize biological changes associated with cancer.

Execute rigorous computational analyses on data from best-in-class molecular assays, including whole genome sequencing, whole genome bisulfite sequencing, targeted sequencing, RNA sequencing, and