Head of AI/ML Research

Head of AI/ML Research

Requirements

• Ph.D. in computer science, statistics, computational biology, or a related field with a focus on machine learning. 15+ years experience, or MS with 21+ years experience, or BS with 23+ years experience
• 10+ years of research experience in AI/ML, computational drug design, structural modeling, or related disciplines within the pharmaceutical or biotechnology industry
• Demonstrated success leading multidisciplinary research projects from conception through to impactful outcomes
• Proven track record of scientific publications and presentations in relevant fields
• Demonstrated expertise in machine learning algorithms, deep learning architectures, transformer-based models, and statistical modeling as applied to molecular design and developability
• Strategic vision for integrating foundation models (e.g., ESM, AlphaFold) into drug discovery workflows with demonstrated impact on therapeutic programs
• Proven track record in developing machine learning models for antibody design, protein–ligand co-folding, and/or related structural modeling approaches
• Proven ability to bridge methodological AI expertise with domain-specific biological knowledge, creating collaborative environments that produce synergistic outcomes
• Proficiency with programming languages (e.g., Python, R, C++) and ML frameworks (e.g., PyTorch, Tensorflow) with experiencing scaling computational infrastructure for large model training in cloud environments
• Exceptional leadership, project management, and communication skills, with ability to translate complex computational insights to diverse scientific audiences and align with therapeutic portfolio priorities and R&D objectives
• Strong problem-solving aptitude and strategic thinking with an entrepreneurial mindset.

Responsibilities

• Develop and execute a research strategy that leverages AI/ML to advance our drug discovery efforts with specific focus on de novo design and optimization of large molecules
• Stay abreast of emerging trends and technologies in AI/ML and their application to pharmaceutical research, including foundation models for protein design, diffusion-based modeling approaches, protein ligand co-folding, generative chemistry, and AI-driven target discovery
• Serve as a central hub connecting AI/ML methodological expertise with domain-specific needs across Computational Science and Data Strategy teams, enabling synergistic collaborations that accelerate innovation
• Drive integration of the modeling pipelines with internal data foundation platforms, building a continuous test-and-learn pipeline through experimental validation by collaborating across scientific disciplines and therapeutic areas
• Recruit, mentor, and manage a high-performing, cross-disciplinary research team focused on the development and implementation of state-of-the-art AI/ML methodologies with an initial focus on computational protein design
• Foster a collaborative, innovative, and inclusive team culture that encourages creative problem-solving and cross-functional collaboration
• Provide regular performance feedback, career development, and guidance to team members
• Oversee the planning, execution, and timely delivery of research projects, ensuring alignment with strategic milestones and corporate objectives
• Ensure robust project management practices are in place, including risk assessment, progress tracking, and resource allocation
• Design partnership models where computational biology teams provide domain expertise while the AI/ML team delivers advanced methodological solutions, creating force-multiplying collaborations
• Champion AI/ML approaches across computational sciences and Research, building cross-functional communities of practice
• Present research findings and strategic updates to internal stakeholders, senior management, and at external scientific conferences
• Establish and nurture collaborative relationships with academic institutions, technology partners, and industry experts
• Contribute to the preparation of patent filings and scientific manuscripts
• Communicate and explain the strengths and weaknesses of complex computational models and ML techniques to diverse scientific audiences
• Drive the development and adoption of novel computational tools and methodologies to enhance our capabilities in computational biology, target discovery, large molecule design and structural modeling
• Design effective strategies for implementing multimodal AI systems that integrate sequence, structure, and functional data for therapeutic candidate optimization
• Evaluate and integrate emerging software and hardware solutions to support advanced AI/ML research initiatives.

Preferred Qualifications

No preferred qualifications provided.