Boehringer Ingelheim is looking to enhance the efficiency and quality of antibody engineering and biotherapeutic research by leveraging advanced machine learning, including protein language models, to accelerate the discovery and development of new biological entities.
Requirements
- Strong foundation in machine learning, deep learning, and statistical modeling, with coursework or project experience in bioinformatics or computational biology.
- Familiarity with protein sequence and structure data, and experience using advanced protein language models (PLMs) such as ESM-2.
- Exposure to structure prediction and generative design tools including AlphaFold, Rosetta, RFDiffusion, and ProteinMPNN.
- Experience working with antibody-specific and structural databases such as SAbDab, OAS, and PDB to support molecular modeling and developability assessments.
- Hands-on experience with graph neural networks (GNNs) for modeling biomolecular interactions and structural relationships.
- Familiarity with AI-driven approaches for modeling protein interactions, structural compatibility, and molecular design.
- Proficiency in Python and relevant libraries (e.g., PyTorch, TensorFlow, scikit-learn).
Responsibilities
- Collaborate with the Biotherapeutics Data Science & AI team to develop and apply generative AI and protein language models for antibody discovery.
- Design and implement deep learning and machine learning models to support predictive analytics in biotherapeutics drug discovery.
- Analyze high-dimensional biological datasets (e.g., sequence, structure, assay data) to uncover insights that inform CMC strategies and improve developability.
- Assist in building scalable pipelines for model training, evaluation, and deployment in a research setting.
- Contribute to ongoing research projects by performing literature reviews, benchmarking algorithms, and presenting findings to cross-functional teams.
- Support the development of internal tools and platforms that accelerate biologics research through automation and intelligent data integration.
Other
- Must be a current undergraduate, graduate or advanced degree student in good academic standing.
- Student must be enrolled at a college or university for the duration of the internship.
- Overall cumulative minimum GPA from last completed quarter/semester 3.0 GPA (on a 4.0 scale) preferred.
- Must be legally authorized to work in the United States without restriction.
- Ability to work independently and collaboratively in a multidisciplinary team environment.
