• Design, adapt, and optimize deep learning architectures for scientific domains and data modalities. • Own and deliver on complex ML projects, including experiment design, implementation, evaluation, and iteration based on results. • Write clean, well-tested code in PyTorch and NumPy enabling a high experimentation rate. • Stay current with deep learning research and its applications in chemistry and biology. • Propose and prototype new ideas to enhance our modeling capabilities. • Work closely with scientists and engineers across the team to integrate models into our product and infrastructure.