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, structural biology and antibody engineering. Drive internal development and external partnerships to create/validate AI/ML tools for next-gen antibody therapies. Collaborate with antibody engineers, structural biologists and AI/ML scientists to shape AI/ML strategy and deliver future biotherapies from state-of-the-art facilities.

Responsibilities

• Utilize AI and machine learning techniques to design novel antibodies and bi/multi-specific antibodies that would be challenging to achieve from screening
• Develop novel AI and ML 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 for structure-based design and engineering
• Leverage deep target biology from broad therapeutic areas to enable desired novel MoAs through molecular design
• Collaborate and support scientists from antibody engineering, therapeutic areas and other tech centers with biotherapeutic design
• Guide and lead AI/ML scientists across multiple functions to have a synergized AI/ML strategy and drive sustained delivery of novel molecular entities to pipeline
• Serve as an expert in computational biologic design to keep up with advancements in AI, ML, and protein engineering
• Maintain data analysis and records in 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
• Strong track record of innovation and research accomplishments in developing AI/ML methods and using deep learning tools to solve antibody/protein engineering challenges
• Experience with structural modeling and design tools (e.g., Rosetta) strongly preferred
• Experience constructing DNA and protein production for screening in silico design preferred
• Proficiency in programming languages such as Python, R and C++
• Ability to work with large datasets and cloud computing infrastructure
• Strong organizational, time-management, and presentation skills

Skills

• Advanced machine learning models related to antibody engineering (language models, geometric deep learning, generative models, multi-modal models)
• Cross-disciplinary collaboration in cross-functional teams
• Data analysis, visualization and clear scientific communication

Education

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