Machine Learning Scientist - Molecular AI

We are seeking an exceptional Machine Learning Scientist to develop next-generation

AI methods for molecular and protein discovery. You will conduct original research

while working closely with engineers to translate new ideas into practical tools that

accelerate scientific discovery.

Our ML team values strong scientific contributions. We are particularly interested in

candidates with a track record of publishing at top-tier machine learning conferences

such as NeurIPS, ICML, ICLR, AISTATS, CVPR, or equivalent venues, or who have

demonstrated research of comparable quality.

Responsibilities

• Develop novel machine learning methods for molecules, proteins, and biological systems.
• Design and evaluate state-of-the-art models using modern deep learning techniques.
• Conduct research on graph neural networks, transformers, diffusion models, and geometric deep learning.
• Work with large-scale molecular, protein, and 3D structural datasets.
• Collaborate with engineers to ensure research can be efficiently implemented and deployed.
• Communicate work internally and externally.
• Stay at the forefront of advances in AI for scientific discovery.

• PhD (or equivalent research experience) in Machine Learning, Computer Science, Computational Biology, Physics, Mathematics, or a related discipline.
• Strong publication record at premier ML conferences (e.g., NeurIPS, ICML, ICLR, AISTATS, CVPR) or equivalent research achievements.
• Deep understanding of modern deep learning methods, including:
• Graph Neural Networks (GNNs)
• Transformer architectures

• Diffusion models

• Strong programming skills in Python and PyTorch.
• Ability to independently drive research from idea to implementation.
• Excellent communication and collaboration skills.

Preferred

• Experience with 3D machine learning, geometric deep learning, or spatial representations.
• Experience working with molecular structures, proteins, drug discovery, or computational chemistry.
• Familiarity with molecular generation, protein modeling, docking, or structure prediction.
• Experience scaling experiments and collaborating with engineering teams to translate research into production systems.