Principal AI​/ML Scientist; Antibody Engineering

Contract Opportunity

Position located in Boston, MA

• Design and implement the computational platform architecture for antibody discovery, integrating ML/DL models, structural biology tools, and high-throughput screening data analysis into the pipeline
• Develop and deploy ML models for antibody-antigen binding prediction, affinity maturation, and develop ability assessment
• Develop state-of-the-art deep learning models for structure-based antibody design, antibody sequence design, antibody-antigen co-folding, and antibody-antigen binding prediction
• Lead the integration of Alpha Fold, Rosetta Antibody/Rosetta Fold, Boltz-1, and other state-of-the-art protein structure prediction tools into the discovery pipeline
• Create and maintain computational workflows for analyzing large-scale screening data (phage display, yeast display, NGS data) to identify lead candidates
• Partner with teammates in Platform Engineering and Discovery to identify key challenges and design innovative, AI-driven solutions using in silico and in vitro validation results
• Implement structural modeling pipelines including homology modeling, molecular dynamics simulations, and protein-protein docking for antibody optimization

• PhD in Computational Biology, Bioinformatics, Computer Science, Physics, or related quantitative field with 6+ years of industry experience in biologics/antibody discovery
• Proven expertise in machine learning and deep learning frameworks (PyTorch, Tensor Flow, JAX) and state-of the-art AI algorithms applied to protein design and structure prediction
• Strong background in protein, molecular dynamics, and antibody-specific computational tools and databases (IMGT, AbNum, Chothia numbering, etc.)
• Skilled in high-throughput data analysis (NGS, display technologies) and structural biology software (PyMOL, Rosetta, Schrödinger suite, MOE)