This position is open to remote within the US or onsite at our headquarters in South San Francisco, CA.
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 Machine Learning Scientist to help grow the Freenome Computational Science team. The ideal candidate will have a strong foundation in Machine Learning, Mathematics, Statistics and Computer Science to incorporate biology in the pursuit of early detection of disease. You will be responsible for leading the scientific direction and execution for the development of early, noninvasive detection tests for multiple cancers. You will also work with computational biologists, molecular biologists and engineers to drive the iteration of research experiments and become the primary drivers towards Freenome’s mission of solving cancer.
You are passionate about innovation and demonstrated initiative in tackling new areas of research, and you will have a significant impact on the continued growth of a high profile technology organization that is changing the landscape on early cancer detection.
The role reports to our Director of Machine Learning Science.
What you'll do:
- Lead the direction and development of cutting edge research in statistical modeling and inference of biological problems (including cancer research, genomics, computational biology/bioinformatics, immunology, therapeutics, and more)
- Lead research projects that propose new methods and perspectives for modeling various biological changes resulting from diseases such as cancer, autoimmune disease, and infection
- Build and immediately apply core analyses in support of a long term research program in data driven biology
- Interface with product teams to identify potential new problem areas in need of an ML solution
- Take a mindful, transparent, and humane approach to your work
- PhD or equivalent research experience in a relevant, quantitative field such as Computer Science (AI or ML emphasis), Statistics, Mathematics, Engineering, or a related field
- 3+ years of post-PhD or industry experience working on the technical subject matter
- Expertise, demonstrated by research publications or industry experience, in applied machine learning, data mining, pattern recognition, or AI
- Strong knowledge of mathematical fundamentals: statistics, probability theory, linear algebra
- Practical and theoretical understanding of fundamental models and algorithms in supervised and unsupervised learning: generalized linear models, kernel machines, decision trees, neural networks; boosting and model aggregation; clustering and mixture modeling; Bayesian inference and model selection, EM, variational inference, Gaussian processes, causal inference, Monte Carlo methods; dimensionality reduction and manifold learning
- Proficiency in a general-purpose programming language: Python, Java, C, C++, etc.
- Familiarity working in a Linux server-based environment
- Excellent ability to clearly communicate across disciplines and work collaboratively towards next steps in experimental iterations
Nice to haves:
- Deep domain-specific experience in computational biology, genomics or a related field
- Experience in scientific parallel computing like an HPE systems, and/or in distributed computing environments like Kubernetes
- Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems
- Experience in high-performance computing, including SIMD or GPU performance optimization
Benefits and additional information:
The US target range of our base salary for new hires is $157,250 - $240,000. You will also be eligible to receive pre-IPO equity, cash bonuses, and a full range of medical, financial, and other benefits dependent 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 expression, veteran status, or any other status protected under federal, state, or local law.
Applicants have rights under Federal Employment Laws.