What is Freenome?
Freenome is a health technology company bringing accurate, accessible and non-invasive disease screenings to you and your doctor to proactively treat cancer and other diseases at their most manageable stages. Freenome is a place where you can do your best, most meaningful work, and contribute to a whole new way of thinking about curing cancer. It’s a data-driven, diverse company at the intersection of biology, technology and medicine.
Equip you and your family with knowledge and tools to maintain a healthier life and prevent disease.
About the Role
As a machine learning engineer at Freenome, you will be an integral part of our R&D team, working in close collaboration with computational biologists and software engineers to develop and deploy the statistical models driving our mission of early detection and intervention in human disease.
- Collaborate within an interdisciplinary technical team to develop your science into Freenome’s product
- Participate in cutting edge research in statistical modeling and inference of biological problems (including cancer research, genomics, computational biology/bioinformatics, immunology, therapeutics, and more)
- Build and immediately apply core analysis technologies to solve patients’ and doctors’ healthcare needs
- Discover science that generalizes 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 solutionTake a mindful, transparent, and humane approach to your work
What We're Looking For
- PhD (or equivalent research experience) in computer science (AI or ML emphasis), statistics, applied math, or a related field
- Expertise, demonstrated by research publications or industrial experience, in applied machine learning, data mining, pattern recognition, or AI
- Experience building statistical models from a variety of input data types: text, images, audio, structured data, time series events, etc
- Proficiency in a general-purpose programming language: Python, Java, C, C++, etc
- Familiarity working in a Linux server-based environment
- 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; EM, variational inference, and local/global optimization; dimensionality reduction and manifold learning
- Ability to clearly communicate across disciplines and work collaboratively towards next steps in experimental iterations
Nice to Haves
- Domain-specific experience in computational biology, genomics or a related field
- Experience in scientific parallel computing
- Experience in high-performance computing, including SIMD or GPU performance optimization
- Experience in a production software engineering environment, including use of automated regression testing, version control, and deployment systems
Our culture is simple. We value Empathy, Trust and Integrity. We feel from the perspectives of each other, patients and the communities we serve. We give each other the benefit of the doubt and believe we’re all working as a team towards our goals. We conduct ourselves with integrity, empowering others to grow in a collaborative environment. Freenome explicitly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.
What does a successful person look like at Freenome?
You can prioritize, manage and execute your goals and align them to the mission of Freenome. You’re a steward of the culture and hold yourself and the team accountable. You believe hiring and mentorship are fundamental to building the organization. You bring ownership to your role, focus on your process and find the gaps to help Freenome reach its full potential. You welcome feedback and criticism knowing its value is to build people up and support each other, rather than tearing each other down.