Associate Director, AI/ML (Biologics)

Position Overview

We are seeking an innovative and dynamic AI/ML Associate Director with a passion for leveraging AI/ML across large molecule drug discovery and development — including antibody design, bispecifics, ADCs, fusion proteins, and other biologics modalities to join our Large Molecule AI/ML team. This role will be part of a multidisciplinary team focused on integrating advanced computational methods with cutting-edge experimental strategies to drive breakthrough discoveries in large molecule therapeutics and deepen our understanding of disease biology. The ideal candidate will have a strong background in computational biology, machine learning, and structural modeling and specifically with the application of AI/ML in diverse biologics modalities, with a demonstrated ability to embed AI/ML capabilities directly within drug programs to address a broad range of discovery and development needs. Beyond technical execution, this Associate Director is expected to operate with a high degree of strategic autonomy: shaping the team’s methodological roadmap, mentoring scientists across functions, and serving as a primary AI/ML voice to senior cross-functional stakeholders in Program teams.

Key Responsibilities

• Develop and implement state-of-the-art AI/ML methodologies for de novo biologics design and optimization across modalities including antibodies, bispecifics, VHH/nanobodies including fine-tuning protein language models and generative protein design.
• Develop, implement, and deploy advanced machine learning algorithms for the multi-objective optimization of antibodies, antigens, ADCs, and other biologics.
• Build tools to incorporate data from integrated Design-Predict-Make-Confirm cycles with automated experimental platforms generating quality data at scale needed for project-specific and foundational models.
• Innovate, develop, and apply predictive models for protein design and developability engineering, utilizing large-scale NGS, in vitro, in vivo and other proprietary in-house and external data sources.
• Manage and process large-scale biological datasets for model training and evaluation.
• Stay abreast of advancements in NLP, ML, and generative AI to build novel tools that enhance therapeutic discovery and development.
• Communicate complex scientific ideas effectively to both technical and non-technical audiences, fostering collaboration across multidisciplinary teams.
• Proactively identify and champion opportunities to apply AI/ML across the drug program portfolio, engaging program teams early to scope computational contributions and translate scientific questions into tractable ML problems.
• Represent the AI/ML function as a scientific and strategic partner to senior cross-functional stakeholders, including Discovery Biology, CMC, and Program Leadership, contributing to portfolio-level decision-making.

Qualifications

• PhD degree in a scientific discipline (or equivalent) with 6+ years relevant experience, or MS with 12+ years relevant experience, or BS with 14+ years relevant experience.
• Proven track record in developing machine learning models for chemical and biological data, including AI/ML-enabled molecular generation and affinity prediction.
• Demonstrated experience in modeling antibody/ antigen sequence, structure and interaction.
• Proficiency in programming languages such as Python and experience with cloud computing capabilities.
• Strong analytical and problem-solving skills, with demonstrated creativity and the ability to contribute individually and collaboratively.
• Versatile communicator who can elucidate complex ideas to non-specialists and commitment to continuous improvement and innovation.
• Demonstrated learning agility, and scientific curiosity while maintaining focus on driving greater impact in the face of uncertainty and change.
• Strong problem-solving aptitude and strategic thinking with an entrepreneurial mindset.

Preferred Qualifications & Skills

• Experience developing or applying modern ML architectures for antibody design models (LLMs, diffusion models, flow-matching, Bayesian Optimization, GNNs, etc.)
• Experience designing de novo binders for specified targets and epitopes.
• Experience analyzing NGS-derived antibody repertoires for sequence- and structure-based design.
• Experience with molecular simulation and conformational analysis techniques.

Additional Information

The position will be based in Cambridge, MA.