Statt Machine Learning Scientist (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 Staff Machine Learning Scientist who will be a part of Freenome’s Computational Science team. The ideal candidate has a strong knowledge of machine learning (ML) fundamentals and deep learning (DL) methods, a track record of successfully answering complex research questions, and the ability to thrive in a highly cross-functional environment. 

They will be responsible for the development of algorithms for early, noninvasive detection tests for multiple cancers. They will build on a foundation of ML/DL and statistical skills to develop models for multiomic molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to drive research experiments and become the primary drivers towards Freenome’s mission of solving cancer.

What you’ll do:

• Independently pursue cutting edge research in artificial intelligence applied to biological problems (including cancer research, genomics, computational biology/bioinformatics, immunology and more)
• Pursue research projects that identify new methods for modeling various biological changes resulting from disease
• Build models that achieve high accuracy, and apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal and biological mechanisms
• Interface with product teams to identify potential new problem areas that can benefit from state of the art ML/DL methods. 
• Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration
• Take a mindful, transparent, and humane approach to your work

Must haves:

• PhD or equivalent research experience with an AI or ML emphasis and in a relevant, quantitative field such as Computer Science , Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics
• 6+ years of post-PhD industry experience working on the technical subject matter
• Expertise, demonstrated by research publications or industry achievements, in applied machine learning, deep learning and complex data modeling 
• Practical and theoretical understanding of fundamental ML models like generalized linear models, kernel machines, decision trees, neural networks; boosting and model aggregation; Bayesian inference and model selection; and variational inference
• Practical and theoretical understanding of DL models like large language models, foundation models, and training paradigms like contrastive learning and self-supervised learning 
• Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data
• Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.
• Proficiency in one or more ML frameworks: Pytorch, Tensorflow, Jax, etc.
• Excellent ability to communicate across disciplines and work collaboratively towards next steps in experimental iterations
• Proficient at productive cross-functional scientific communication and collaboration with software engineers and computational bi