The Chan Zuckerberg Initiative (CZI) aims to solve tough societal challenges, including eradicating disease and improving education. The AI/ML team is building a large computing system for nonprofit life sciences research to provide access to predictive models of healthy and diseased cells, ultimately helping researchers cure, prevent, or manage all diseases by the end of the century.
Requirements
- Experience in modern computer vision methods such as vision transformers or diffusion models.
- Experience in developing and applying methods utilizing time series and longitudinal data, representation learning, geometric learning and GNNs or neural rendering.
- Proven expertise in machine learning, with hands-on experience designing and experimenting with deep learning architectures, analyzing data, and visualizing results.
- Hands-on experience with deep learning frameworks (e.g. PyTorch, JAX) and a solid understanding of neural network architectures for tasks like time series and/or generative modeling.
- Have a proven track record of research and innovation in machine learning, computer vision or computational biology, demonstrated through publications, conference presentations, or project outcomes with an ability to stay updated with the latest advancements in the field.
Responsibilities
- Develop and apply state-of-the-art methods in artificial intelligence such as representation learning, time series modeling or physics-informed machine learning to solve important problems in biological imaging.
- Work with a diverse range of biological data types, including cell/tissue images, time-lapse microscopy, cryo-ET tomography, multi-omics data and more.
- Implement scalable solutions with respect to complex problems/scientific questions on large-scale data sets, especially using machine learning approaches, predictive models, and statistical analysis, to advance understanding of cell dynamics, systems, and interactions.
- Work as part of a team to leverage large open-source biological datasets and large-scale compute to create models that transform our understanding of cells.
- Advance the community standards for dissemination, presentation, and evaluation of computational approaches to core scientific problems.
- Contribute to the scientific community through publishing papers, blog posts, open source code, and attending conferences in machine learning and the life sciences.
Other
- Have a PhD or Masters in computer science (focus on machine learning and/or computer vision), physics or applied math, or a related field or equivalent industry experience and at least 5 years of experience developing and applying state-of-the-art AI/ML methods.
- Enjoy working in a highly interactive and cross-functional collaborative environment with a diverse team of colleagues and partners in leading-edge cell biology data-driven research.
- Have the ability to work independently and as part of a team, and have excellent communication and interpersonal skills.
- This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team’s manager.
