Research Senior Scientist, AI/ML (Biologics)

Role Summary

Role: Research Senior Scientist, AI/ML (Biologics). Location: Cambridge, MA. We are seeking an innovative AI/ML Research Senior Scientist with a passion for leveraging AI/ML in antibody discovery and design to join our Large Molecule AI/ML team. The role focuses 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.

Responsibilities

• Develop and implement state-of-the-art AI/ML methodologies for de novo antibody design and discovery, 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.
• Collaborate with internal experts to optimize the computational discovery infrastructure, offering both individual and team-based innovative solutions.
• Communicate complex scientific ideas effectively to both technical and non-technical audiences, fostering collaboration across multidisciplinary teams.

Qualifications

• PhD degree in a scientific discipline (or equivalent) with 2+ years relevant experience, or MS with 8+ years relevant experience, or BS with 10+ 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.

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

Education

• PhD in a scientific discipline (or equivalent); or MS/BS with corresponding years of relevant experience as an alternative pathway.