Research Scientist, AI/ML Biologics - Methods Development - Method

Role Summary

Role: Research Scientist, AI/ML Biologics - Methods Development. Location: Boston, MA. You will develop and apply machine learning methods to accelerate antibody discovery and optimization on active pipeline projects, collaborating with protein engineers, computational scientists, and experimental teams to deliver predictive models that impact candidate selection and developability assessment.

Responsibilities

• Develop and implement machine learning models for antibody property prediction, including developability attributes (stability, aggregation, immunogenicity, viscosity) to support active discovery programs.
• Build predictive tools that rank antibody candidates, flag potential liabilities, and suggest sequence modifications for improved properties.
• Benchmark and evaluate external computational methods and commercial AI platforms; recommend best-in-class tools for integration into internal workflows.
• 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.
• Investigate transfer learning and few-shot learning approaches to enable rapid model deployment on new antibody formats (multi-specifics, VHH, ADCs) with limited training data.
• Collaborate with experimental teams to validate predictions against assay data, iterate on model development, and integrate AI/ML outputs into Design-Predict-Make-Confirm cycles.
• Establish and maintain AI performance dashboards and KPIs to track prediction accuracy, model reliability, and impact on project timelines.
• Stay current with advances in machine learning for protein science and contribute to internal knowledge sharing.

Qualifications

• Required: PhD in Computational Biology, Bioinformatics, Computer Science, or related field, OR MS with 6+ years relevant experience, OR BS with 10+ years relevant experience.
• Proven track record in developing machine learning models for biological or chemical data.
• Proficiency in Python and machine learning frameworks (PyTorch, TensorFlow, or scikit-learn).
• Experience with protein sequence analysis and understanding of antibody structure-function relationships.
• Strong analytical and problem-solving skills with demonstrated ability to work both independently and collaboratively.
• Excellent communication skills to convey complex computational concepts to diverse scientific audiences.

• Preferred: Experience with protein language models (ESM, ProtTrans) or other deep learning architectures for protein property prediction.
• Familiarity with antibody developability assessment (stability, aggregation, immunogenicity).
• Experience with transfer learning or active learning approaches.
• Prior experience in pharmaceutical or biotech R&D environment.
• Experience with cloud computing (AWS, GCP) and version-controlled ML pipelines.

Education

• PhD in Computational Biology, Bioinformatics, Computer Science, or related field, or MS with 6+ years relevant experience, or BS with 10+ years relevant experience.
• Strong foundation in machine learning, biology, and statistics as applicable to protein science.