Senior Software Engineer II, Research Platform (Remote)

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 ~500 employees and more than $1.1B in funding from key investors, such as the American Cancer Society, Andreessen Horowitz, Anthem Blue Cross, Bain Capital, Colorectal Cancer Alliance, DCVC, Fidelity, Google Ventures, Kaiser Permanente, Novartis, Perceptive Advisors, RA Capital, Roche, Sands Capital, T. Rowe Price, and Verily.  

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:

At Freenome, we are seeking a Senior Software Engineer to build the next generation Research Platform that accelerates our scientific mission to develop accessible solutions to combat cancer. In this role you will be at the core of a world-class interdisciplinary group including ML engineers, Data Scientists, Bioinformatics, Computational Biologists. You will closely partner with Product Managers and other Infrastructure and Software Engineers to drive innovation, contributing to the success of Freenome as a whole. The ideal candidate is comfortable working in a dynamic, flexible environment and adapting to rapid technological progress and changing research priorities. 

The role reports to our engineering management team. 

What you’ll do:

• Lead development and scaling efforts on the computing and data platforms that power all Freenome’s research
• Understand user needs across a wide range of scientific disciplines and create simple solutions that scientists can use productively
• Build tools and infrastructure to orchestrate and track distributed workflows, supporting both petabyte-scale data pipelines and machine learning model development
• Develop an advanced Data Lakehouse with support for high throughput data access
• Collaborate with team members through code and design review and pairing
• Mentor other engineers, fostering the growth your team’s technical expertise
• Provide technical support and pair as needed with scientific partners
• Champion platform and data engineering best practices across all Freenome

Must haves:

• 6-8 years of experience as a member of software teams that have shipped to production highly reliable platforms serving a diverse set of users
• Fluency with one or more high-level programming languages, preferably Python or Go
• Experience with Docker and container orchestrators such as Kubernetes
• Experience architecting distributed systems in GCP or other cloud providers
• Passion for CI/CD, Infrastructure as Code and DevOps best practices
• Experience profiling and debugging at system level, and leveraging Observability tools
• Experience with multiple datastore types, including SQL, NoSQL, columnar, timeseries
• Adaptability to thrive in a highly collaborative environment, engaging with technical and scientific partners a daily basis, while navigating ambiguous requirements
• Excellent written/verbal communication and documentation skills

Nice to haves:

Experience or familiarity with some of the following:

• Building platforms to accelerate data engineering, research and machine learning
• Distributed computing frameworks and design patterns
• Flyte or other modern workflow orchestrators like Metaflow, Argo Workflows
• Terraform, Pulumi or other Infrastructure as Code tools
• Apache Parquet or other similar columnar data formats
• Apache Iceberg or other data lake technologies like DeltaLake, Apache Hudi
• Distributed query engines such as Apache Spark or Trino/Presto

Domain-specific experience in machine learn