Staff Bioinformatics Research Engineer

Why join Freenome?

Freenome is a high-growth biotech company developing tests to detect cancer using a standard blood draw. To do this, Freenome uses a multiomics platform that combines tumor and non-tumor signals with machine learning to find cancer in its earliest, most-treatable stages. 

Cancer is relentless. This is why Freenome is building the clinical, economic, and operational evidence to drive cancer screening and save lives. Our first screening test is for colorectal cancer (CRC) and advanced adenomas, and it’s just the beginning. 

Founded in 2014, Freenome has ~400 employees and continues to grow to match the scope of our ambitions to provide access to better screening and earlier cancer detection.  

At Freenome, we aim to impact patients by empowering everyone to prevent, detect, and treat their disease. This, together with our high-performing culture of respect and cross-collaboration, is what motivates us to make every day count.

Become a Freenomer

Do you have what it takes to be a Freenomer? A “Freenomer” is a determined, mission-driven, results-oriented employee fueled by the opportunity to change the landscape of cancer and make a positive impact on patients’ lives. Freenomers bring their diverse experience, expertise, and personal perspective to solve problems and push to achieve what’s possible, one breakthrough at a time.

About this opportunity:

As a Staff 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 and software engineers. You will develop cutting edge solutions at the intersection of machine learning, genetic sequencing technology, biological data, and distributed systems. These contributions will drive our mission to diagnose cancer at its most actionable and early stages.

What you’ll do:

• Drive the development of the computational research platform so that bioinformatics, in conjunction with other functions such as machine learning and model development, will enable rapid evaluation of scientific hypotheses

• Connect the biological research that leverage computational biology fundamentals related to NGS or multi-omic data sources to the production system that will process patient samples at scale

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

• Lead the development of Freenome’s bioinformatics research 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

• 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

Must haves:

• PhD in a quantitative field such as computational biology, cancer biology, statistics, bioinformatics, computer science, or B.S./M.S. and equivalent experience

• 5+ years of experience with bioinformatics infrastructure, automation, and software engineering

• Robust history of delivering major technical projects or analyses, including experience in a technical leadership role

• 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

Nice to haves:

• 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

Benefits and additional information:

The US target range of our