Machine Learning Scientist (Remote)

Why join Freenome?

Freenome is a rapidly growing biotech company that develops tests to detect cancer using a standard blood draw. Freenome employs a multiomics platform that combines tumor and non-tumor signals with machine learning to detect cancer in its earliest and most treatable stages.

Cancer is relentless. That's why Freenome is compiling clinical, economic, and operational evidence to advance cancer screening and save lives. Our first screening test is for colorectal cancer (CRC) and advanced adenomas, but that’s only the beginning.

Founded in 2014, Freenome has approximately 500 employees and has received over $1.1B in funding from key investors including 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 strive to affect patient lives by enabling everyone to prevent, detect, and treat their disease. This, together with our performance-driven culture of respect and cross-collaboration, is what pushes us to make every day count.

Become a Freenomer

Do you have what it takes to be a Freenomer? A “Freenomer” is a committed, mission-driven, results-oriented employee inspired by the opportunity to change the landscape of cancer and positively impact patients’ lives. Freenomers leverage their diverse experiences, expertise, and personal perspectives to solve problems and strive to achieve what's possible, one breakthrough at a time.

About this opportunity:

At Freenome, we are seeking a Staff Machine Learning Scientist to join our Computational Science team. The ideal candidate has a robust understanding of machine learning (ML) fundamentals and deep learning (DL) methods, a proven track record of successfully addressing complex research questions, and the ability to excel in a highly cross-functional environment.

Their role will involve developing algorithms for early, noninvasive detection tests for multiple cancers. They will utilize their skills in ML/DL and statistics to build models for multiomic molecular signals from blood. They will also collaborate with computational biologists, molecular biologists, and ML engineers to guide research experiments and become primary contributors to Freenome’s mission of conquering cancer.

What you’ll do:

• Independently undertake cutting-edge research in artificial intelligence applied to biological problems (including cancer research, genomics, computational biology/bioinformatics, immunology, and more).
• Initiate research projects that identify new methods for modeling various biological changes resulting from disease.
• Develop models that reach high accuracy, and utilize contemporary interpretation techniques to provide a deeper understanding of the underlying signal and biological mechanisms.
• Interact with product teams to identify potential new problem areas that could benefit from the latest ML/DL methods.
• Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration.
• Adopt a mindful, transparent, and humane approach to your work.

Must haves:

• PhD or equivalent research experience with an AI or ML emphasis 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 technical subjects.
• 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.
• Proficiency in current advancements in ML/DL approaches in various 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 in productive cross-functional scientific communication and collaboration with software engineers and computational biologists.
• A passion for innovation and demonstrated initiative in exploring new areas of research.

Nice to haves:

• Deep domain-specific experience in computational biology, genomics, proteomics, or a related field.
• Experience in NGS data analysis and bioinformatic pipelines.
• Experience with containerized cloud computing environments, such as Docker in GCP or AWS.
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems.

Benefits and additional information:

The US target range for our base salary for new hires is $182,750 - $280,000. You will also be eligible to receive pre-IPO equity, cash bonuses, and a complete range of medical, financial, and other benefits depending on the position offered. Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @ https://careers.freenome.com/ for additional company information.

Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or