Scientist, Machine Learning and Computational Biology

Job Overview

23andMe is seeking exceptional researchers with backgrounds in machine learning, statistics, physics, applied mathematics, and/or computational biology. Successful candidates will have a passion for independent research and technical problem-solving, as well as a demonstrated ability to develop and implement research-based ideas.

Who We Are

Since 2006, 23andMe’s mission has been to help people access, understand, and benefit from the human genome. We are a group of passionate individuals pushing the boundaries of what’s possible to help turn genetic insight into better health and personal understanding. With the world’s largest database of more than fifteen million genotyped customers, 23andMe is at the forefront of using human genetics to advance biomedical research and transform healthcare.

What You’ll Do

In this role, you will design and develop new algorithms and analysis tools that combine genetic variation information with other molecular, cellular, and phenotypic data. You will be implementing, extending, and debugging cutting-edge machine learning models, as well as designing and building robust data infrastructure and tools. You will also collaborate to complete complex projects and present findings to other 23andMe scientific teams as well as the broader scientific community.

Ideal candidates will have demonstrated expertise in developing deep learning techniques and applying them to biological and healthcare data. Relevant areas of experience could include language/sequence modeling, graph embeddings, representation learning, generative models, and self-supervised learning techniques. However, specific domain knowledge in any of these areas is less important than genuine intellectual curiosity, strong research skills, and a track record of scientific innovation and achievement.

What You’ll Bring

• Ph.D. in the physical or life sciences (e.g. Chemistry, Biology, Physics), or quantitative field (e.g. Computer Science, Statistics, Mathematics)
• Deep understanding of computational biology, statistical genetics, and/or machine learning
• A track record of academic or industrial accomplishments, including the development of novel methods and analytical approaches for large-scale data analysis
• Strong R or Python skills, and experience with Python-based machine learning libraries (Pytorch, TensorFlow, etc)
• Experience developing methods utilizing modern neural network architectures, including Transformer, Graph Convolutional, and Diffusion models.
• Exceptional communication skills, with an ability to convey complex technical concepts and scientific insights to diverse audiences
• Ability to work in a dynamic team-oriented environment
• Experience conducting research with EHR data is a plus
• Experience with disease areas such as metabolic diseases, immune diseases, or oncology is a plus
• Ability to work 3 days onsite (Tuesday-Thursday) in our Sunnyvale, CA office

About Us

23andMe is a genetics-led consumer healthcare and biopharmaceutical company empowering a healthier future. Founded in 2006, the company’s mission is to help people access, understand, and benefit from the human genome. 23andMe has pioneered direct access to genetic information as the only company with multiple FDA authorizations for genetic health risk reports. The company has created the world’s largest crowdsourced platform for genetic research, with 80 percent of its customers electing to participate.

At 23andMe, we value a diverse, inclusive workforce and we provide equal employment opportunity for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual’s race, color, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws.