Senior Principal Scientist AI/ML Antibody Design and Engineering

Role Summary

We are seeking a Senior Principal Scientist in AI/ML to lead the development of next-generation antibody therapeutics. This role sits at the intersection of computational biology, machine learning, and antibody engineering; building AI-driven platforms that accelerate therapeutic discovery from concept to clinic. You will drive the internal build and external partnerships required to create and validate AI/ML platforms, including large language models and in silico protein design tools. Working closely with antibody engineers, structural biologists, and AI/ML scientists, you will shape strategy, guide technical execution, and translate advanced computational methods into impactful biotherapies. This is a rare opportunity to define how AI/ML is embedded into antibody discovery, influence platform-level decisions, and deliver novel therapeutics from world-class research facilities.

Responsibilities

• Develop novel AI and machine learning tools to enable de novo antibody discovery with unique properties and in silico co-optimization of affinity, expression, stability and PK/half-life.
• Apply deep learning and generative AI techniques to train/enhance LLM or other relevant language models using internal datasets.
• Use in silico protein/antibody engineering design tools such as Rosetta to drive for structure-based design and engineering.
• Collaborate and support scientists from antibody engineering, therapeutic areas and other tech centers to deliver novel molecular entities to our pipeline.
• Serve as an expert in computational biologic design to keep up with the latest advancements in AI, machine learning, and protein engineering fields.
• Maintain data records and present to cross-functional teams.

Qualifications

• Required: PhD in Computational Biology, Bioinformatics, Computer Science, Structural Biology or a related field, with 7–10 years of relevant experience.
• Required: Proficiency in programming languages such as Python, R and C++.
• Required: Strong track record of innovation and research accomplishments in developing AI/ML methods and using deep learning tools to solve antibody/protein engineering challenges.
• Preferred: Experience in using structural modeling and design tools (e.g., Rosetta).
• Note: Candidates with PhD only in a relevant field will be considered at the Principal level as well.

Skills

• Computational biology
• Antibody design and engineering
• AI/ML methods and deep learning
• Large language models and generative models
• Structural modeling and design tools (Rosetta)

Education

• PhD in Computational Biology, Bioinformatics, Computer Science, Structural Biology or a related field