Machine Learning Scientist/Sr Scientist - Antibody Property Prediction & Generative Design

Role Summary

Machine Learning Scientist/Sr Scientist - Antibody Property Prediction & Generative Design. Role focuses on antibody and biologic drug development within the TuneLab AI-powered drug discovery platform, applying advanced machine learning to accelerate antibody discovery, optimization, and developability assessment across a federated network.

Responsibilities

• Antibody Property Prediction: Build multi-task learning frameworks for antibody properties (binding affinity, specificity, stability, immunogenicity, developability metrics) from sequence and structural features.
• Antibody Sequence Generation: Develop generative models for antibody design, including CDR optimization, humanization, and affinity maturation while maintaining structural integrity.
• Structure-Aware Design: Integrate structural modeling with generative approaches to ensure proper folding, CDR conformations, and epitope recognition.
• Developability Optimization: Create models optimizing expression yield, solubility, viscosity, and post-translational modifications for manufacturing.
• Species Cross-Reactivity: Design antibodies with desired species cross-reactivity profiles for preclinical development.
• Antibody-Antigen Modeling: Predict antibody-antigen interactions, epitope mapping, and paratope design using sequence and structural information.

Qualifications

• Required: PhD in Computational Biology, Protein Engineering, Immunology, Biochemistry, or related field from an accredited institution.
• Required: Minimum of 2 years of experience in antibody or protein therapeutic development within the biopharmaceutical industry.
• Required: Strong experience with protein sequence analysis and structural biology.
• Required: Proven track record in machine learning applications to biological sequences.
• Required: Deep understanding of antibody structure-function relationships and immunology.

Additional Preferences

• Experience with immune repertoire sequencing and analysis.
• Publications on antibody design, protein engineering, or therapeutic development.
• Expertise in protein language models and transformer architectures.
• Knowledge of antibody manufacturing and CMC considerations.
• Experience with display technologies (phage, yeast, mammalian).
• Understanding of clinical immunogenicity and prediction methods.
• Proficiency in protein modeling tools (Rosetta, MOE, Schrodinger BioLuminate).
• Familiarity with antibody-drug conjugates and bispecific platforms.
• Experience with federated learning in biological applications.
• Portfolio mindset balancing innovation with practical developability.

Education

• PhD in relevant field (as listed under Qualifications).

Additional Requirements

• Location: Indianapolis, IN; or South San Francisco, CA; or Boston, MA with up to 10% travel to attend key industry conferences. Relocation provided.