Staff Research Scientist – AI x Imaging – Science
| Company | Chan Zuckerberg Initiative |
|---|---|
| Location | San Carlos, CA, USA |
| Salary | $214000 – $321000 |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior, Expert or higher |
Requirements
- Have a PhD or Masters in computer science (focus on machine learning and/or computer vision), computational biology, or a related field or equivalent industry experience and at least 5 years of experience developing and applying machine learning methods.
- Experience in representation learning techniques, including expertise in unsupervised and self-supervised learning methods, dimensionality reduction, and feature extraction and the ability to create meaningful and high-quality data representations from image data that facilitate downstream tasks.
- Hands-on experience with deep learning frameworks (e.g. PyTorch, TensorFlow, JAX) and a solid understanding of neural network architectures for tasks like image recognition, segmentation, and 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.
- 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.
Responsibilities
- Develop and apply state-of-the-art methods in artificial intelligence and machine learning to solve important problems in biological imaging such as representation learning, multi-object tracking, multimodal data integration or time series modeling.
- Work with a diverse range of biological data types, including multi-omics data, cell/tissue images, time lapse microscopy, cryo-ET tomography 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 structures, systems, and interactions.
- Work as part of a team to leverage large open-source biological datasets 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.
Preferred Qualifications
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No preferred qualifications provided.