Senior Bioinformatics Research Engineer

As a Senior Bioinformatics Research Engineer, you will work on Freenome’s computational platform for R&D, developing and deploying bioinformatics research software and tooling. As part of an interdisciplinary R&D team, you will work in close collaboration with computational biologists, machine learning scientists an...

The role reports to the Director of Bioinformatics Research Engineering. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.

Work at the intersection of computational biology, machine learning research, software engineering and biological data.

Lead the development of Freenome’s bioinformatics research engineering framework and internal tools.
Build for significant growth and scaling challenges as we transition from research to product development.
Engineer robust and reproducible data pipelines that enable cutting edge liquid biopsy research.
Manage and integrate data sources across the company (laboratory, bioinformatics, clinical) to enable advanced multiomic analysis.
Improve the reliability and scalability of our science platform as it grows.
Develop software and workflows that support a long term vision for bioinformatics research and production.
Enable data driven science discoveries in biology and healthcare.

PhD in a quantitative field such as computational biology, cancer biology, statistics, bioinformatics, computer science, or B.S./M.S. and equivalent experience.
4+ years of experience with bioinformatics infrastructure, automation, and software engineering.
Robust history of delivering major technical projects or analyses.
Strong computational and programming skills, in Python or equivalent, including thorough experience with large scale systems and user bases.
Fundamental understanding of bioinformatics, including the central dogma, molecular or cancer biology, or familiarity with regulatory processes. 
Experience in NGS data analysis and bioinformatic pipelines. 
Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS.
Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems.
Demonstrated ability to partner with laboratory and computational scientists to translate scientific roadmaps into concrete products.
Ability to operate in a highly cross-functional environment where collaboration across disciplines is absolutely necessary.
Experience in applying statistical methods and algorithms to large-scale datasets.
Experience in analyzing one or more of the following biological modalities: epigenomics, proteomics (including mass spectrometry), transcriptomics (RNA-seq).
Track record of selflessly supporting and growing highly effective teams.