Define and build end-to-end ML/AI systems, including data creation, data cleaning/processing, model development, training, inference, and deployment. Lead R&D efforts on novel models for understanding the intersection of chemistry and biology. Architect and scale large models trained on paired chemistry and biological imaging data.
Requirements
- Strong background in machine learning, particularly multi-modal, self-supervised, or unsupervised learning with imaging data.
- Experience in applied computer vision research and model development.
- Deep learning expertise applied to challenging or non-standard modeling problems.
- Familiarity with molecular property prediction, generative modeling, or ML for chemistry and biology.
- Experience with cell imaging data or other biological imaging modalities is highly valued.
- Track record of impactful research and/or production ML systems.
Responsibilities
- Define and build end-to-end ML/AI systems, including data creation, data cleaning/processing, model development, training, inference, and deployment.
- Lead R&D efforts on novel models for understanding the intersection of chemistry and biology.
- Architect and scale large models trained on paired chemistry and biological imaging data.
- Explore and drive innovation in molecular graph representations, contrastive learning for chem/bio, semi-supervised learning on biological images, and generative models (e.g., diffusion) for biological data.
- Collaborate with scientists and engineers to translate cutting-edge ML techniques into impactful products and tools.
- Contribute to both research leadership and product development, taking projects from concept to real-world application.
Other
- Ability to thrive in a fast-moving, entrepreneurial environment and contribute to projects from 0 to 1.
- Establish the foundation of a strong research culture focused on advancing applied ML in science.
