We are looking for a Senior AI Researcher to join our foundation models team and help advance large-scale models for single-cell RNA (scRNA) data.
In this role, you will focus on training and improving transformer-based and deep learning models, developing algorithmic solutions to challenging modeling problems, and adapting modern foundation model techniques to biological data. This is a hands-on research position for someone who enjoys building and training deep models, understanding their behavior, and pushing them to perform better.
You do not need prior biology experience - but you should be excited to apply your deep learning expertise to a new scientific domain.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Develop, train, and refine transformer-based and related architectures for large-scale scRNA data.
Tackle challenging modeling problems with thoughtful, principled approaches.
Apply ideas from NLP and vision (pretraining, self-supervision, transfer learning) to biological data.
Plan careful experiments, perform ablations, and deeply analyze results to guide model improvements.
Engage directly with large, noisy biological datasets and build intuition for model behavior in this setting.
Partner with engineers, data scientists, and biology experts to turn research ideas into working systems.
Requirements: Required qualifications:
MSc or PhD in Computer Science, Mathematics, Physics, or a related scientific field.
5+ years of hands-on, industry experience training deep learning models (e.g., transformers, vision models, or related architectures).
Experience with large-scale pretraining and foundation models.
Strong engineering background, specifically building machine learning infrastructure for training large models.
Strong practical experience with PyTorch (or similar frameworks).
Solid understanding of optimization and representation learning.
Strong experience in experimental design, statistical evaluation, and working with large, complex datasets.
Ability and willingness to adapt to a new domain (biology).
Preferred qualifications:
Experience adapting methods across domains.
Research publications or meaningful research contributions.
Experience in computational biology or biomedical data (a plus, not required).
Desired personal traits:
You want to make an impact on humankind.
You prioritize We over I.
You enjoy getting things done and striving for excellence.
You collaborate effectively with people of diverse backgrounds and cultures.
You have a growth mindset.
You are candid, authentic, and transparent.
This position is open to all candidates.