Key Responsibilities

• Participate in cutting-edge research in ML, 3D generative models, and applications to drug discovery

• Collaborate with cross-functional teams to deliver an impactful, business-critical project

• Develop well-documented code to facilitate adoption of the method

• Present results in the form of a publication, for submission to internal and external scientific conferences

Program Highlights

• Intensive 12-week, full-time (40 hours per week) paid internship

• Program start dates are in May/June (Summer)

• A stipend, based on location, will be provided to help alleviate costs associated with the internship

Who You Are (Required) 

Required Education

• Current Ph.D. student in Computer Science, Engineering, Statistics, Applied Mathematics, Computational Biology, Computational Chemistry, Physics, or related technical field.

Required Skills

• Strong publication record or evidence of impactful research contributions (e.g., NeurIPS, ICML, ICLR, AISTATS, TMLR, CVPR, ICCV/ECCV) 

• Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX)

• Experience in at least one of the following areas: 

• Reinforcement learning or preference learning

• Generative modeling (e.g., diffusion models, autoregressive models)

• Graph neural networks or molecular representation learning

Preferred Skills

• Experience with RLHF methods (e.g., DPO, PPO-RLHF, reward modeling)

• Familiarity with molecular modeling, cheminformatics, or drug discovery applications

• Contributions to open-source ML frameworks or reproducible research environments

• Excellent communication, collaboration, and interdisciplinary working skills