Blood-based cancer detection platform using multiomics and machine learning
Freenome develops blood tests for early-stage cancer detection using multiomics data and computational biology. The tech stack reveals a hybrid biotech-software operation: laboratory infrastructure (Illumina, LIMS, Veeva Vault) sits alongside cloud platforms (GCP, AWS, Azure) and ML tooling (Airflow, Flyte, Python). Current hiring skews research-heavy (8 roles) with meaningful engineering investment (5 roles), while active projects span distributed deep-learning pipelines and NGS workflow optimization—signaling the company is scaling computational infrastructure to handle genomic data at volume.
Notable leadership hires: Finance Director
Freenome is a biotech company founded in 2014 focused on blood-based cancer screening. The company has developed a multiomics platform designed to detect early-stage colorectal cancer and advanced adenomas through machine learning analysis of cell-free DNA and other blood biomarkers. The organization operates across three main areas: wet-lab research and assay development; computational biology and ML model training; and clinical adoption and commercialization. Freenome is based in Brisbane, California, and has raised over $1.1 billion from investors including Roche, Novartis, and Kaiser Permanente, among others.
Freenome uses a multiomics platform that analyzes blood samples to detect early-stage colorectal cancer and advanced adenomas. The approach combines cell-free DNA analysis with machine learning and computational biology techniques.
Freenome's stack spans cloud (GCP, AWS, Azure), containerization (Kubernetes, Docker), data processing (Apache Airflow, Flyte), databases (MySQL), lab informatics (Veeva Vault, LIMS, Illumina), and analytics (Tableau, Power BI, Looker). The company is currently adopting AWS EMR.
Freenome's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.