Senior AI Scientist - Peptide Design

Role Summary

Senior AI Scientist to join AbbVie's Madison Peptide Therapeutics team. You will drive adoption and development of generative AI methods to accelerate peptide drug discovery, leveraging a proprietary peptide synthesis platform capable of synthesizing millions of unique peptide sequences with canonical and non-canonical amino acids. The successful candidate will identify and develop novel algorithms and pipelines and collaborate closely with both computational and experimental scientists to guide and optimize AI-enabled hit generation strategies.

Responsibilities

• Develop machine learning and computational chemistry methods to enable discovery of peptides and/or peptide-containing scaffolds that bind to target proteins
• Lead efforts to leverage various computational chemistry tools and machine learning architectures to accelerate design of peptide-based molecules
• Lead deployment of machine learning algorithms across various therapeutic areas
• Build data sets and develop novel strategies to quantify model performance
• Present results at internal and external scientific conferences
• Routinely demonstrate scientific initiative and creativity in research activities
• Formulate conclusions and design follow-on experiments based on multidisciplinary data
• May initiate new areas of investigation that are scientifically meaningful, reliable, and can be incorporated directly into a research or development program
• Author of publications, presentations, regulatory documents and/or primary inventor of patents
• Understand and adhere to corporate standards regarding code of conduct, safety, appropriate handling of materials, controlled drug and radioactive compounds, GxP compliance, and animal care where applicable
• Direct mentorship of others

Qualifications

• Required: Bachelor’s Degree or equivalent education and typically 10 years of experience, Master’s Degree or equivalent education and typically 8 years of experience, PhD and no experience necessary
• Required: Proven experience developing deep learning approaches for peptide generation and peptide drug design
• Required: Experience with protein structure modeling, design, or prediction algorithms, spanning physics-based to deep learning architectures
• Required: Fluent in Linux command-line, python, and version control (git)
• Required: Demonstrated record of research excellence in machine learning as evidenced by conference presentations (e.g. NeurIPS) and journal publications
• Required: Thorough theoretical and practical understanding of relevant scientific disciplines
• Required: Applied experience in a quantitative science (e.g., Chemistry, Biology, Biochemistry, etc.)
• Required: Extensive experience working with large chemical and biological datasets, including graph, sequence, and structure-based data
• Required: Hands-on experience building and training models, applying transfer learning, and/or fine-tuning models using deep learning frameworks (such as PyTorch)
• Required: Experience using cloud computing, high-performance computing, or GPU clusters
• Required: Ability to work collaboratively on projects with multiple contributors
• Required: High level of autonomy and productivity in laboratory research or method development, requiring minimal supervision
• Preferred: Experience developing deep generative approaches for cyclic peptide drug design
• Preferred: Experience developing deep generative approaches for peptides with non-canonical amino acids
• Preferred: Experience developing novel strategies to quantify model performance, especially for diffusion, flow-matching, and transformer-based models
• Preferred: Experience contributing to drug discovery efforts