Senior Principal Scientist, AI/ML Antibody Design & Engineering

Role Summary

Senior Principal Scientist specializing in AI/ML to lead the design and engineering of next-generation antibody-based protein therapies. Focus on computational biology, programming, large language models, in silico protein design tools, and antibody engineering to enable AI/ML tools and biotherapeutic design in collaboration with cross-functional teams.

Responsibilities

• Utilize AI and machine learning techniques to design novel antibodies and bi/multi-specific antibodies that are challenging to achieve from screening
• Develop AI/ML tools to enable de novo antibody discovery with 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 language models using internal datasets
• Use in silico protein/antibody engineering design tools such as Rosetta for structure-based design and engineering
• Leverage deep target biology to enable novel mechanisms through molecular design
• Collaborate with antibody engineering, therapeutic areas and other tech centers with biotherapeutic design
• Guide and lead AI/ML scientists across functions to drive a synergized AI/ML strategy and pipeline delivery
• Serve as an expert in computational biologic design and stay current with AI, ML, and protein engineering advancements
• Maintain data analysis and records in a well-organized fashion
• Present data clearly to teams and management

Qualifications

• PhD in Computational Biology, Bioinformatics, Computer Science, Structural Biology or related field
• 7-10 years of relevant experience with a strong track record in AI/ML methods for antibody/protein engineering
• Experience with structural modeling/design tools (e.g., Rosetta) preferred
• Experience constructing DNA and protein production for in silico design preferred

Skills

• Computational biology, programming (Python, R, C++), deep learning, language models, generative and multimodal models
• Antibody engineering, structural biology, in silico protein design
• Cloud computing and handling large datasets
• Strong collaboration, organizational, time-management and presentation skills

Education

• PhD as listed in Qualifications

Additional Requirements

• No dedicated travel or physical demands specified as essential in the description