Position located in Boston, MA

Responsibilities:

• Develop and apply machine learning and computational modeling approaches to accelerate therapeutic discovery across oligonucleotide and biologic platforms.
• Build predictive and generative models to support antibody engineering, including antibody–antigen interaction modeling, sequence analysis, structural prediction, and de novo protein design.
• Apply AI/ML techniques to identify and rank promising ASO candidates based on sequence characteristics, target accessibility, exon-skipping activity, and other relevant biological parameters.
• Develop computational strategies for optimizing antibodies, antigens, ADCs, oligonucleotides, and other emerging therapeutic modalities against multiple design objectives.
• Create scalable, reproducible workflows spanning data preparation, feature generation, model development, training, evaluation, and implementation.
• Integrate sequence, structural, biochemical, and experimental datasets from internal programs, published literature, and external sources to improve model performance and biological insight.
• Investigate and incorporate relevant molecular descriptors, including sequence motifs, thermodynamic properties, structural accessibility, secondary structure, binding characteristics, and other predictive features.
• Establish rigorous model evaluation, benchmarking, and validation strategies and work closely with laboratory scientists to test computational predictions experimentally.
• Assess emerging AI/ML methodologies, commercial platforms, open-source packages, and protein/oligonucleotide modeling technologies for potential integration into discovery workflows.
• Develop well-structured, maintainable code and computational documentation that enables scientists across multidisciplinary teams to effectively use and interpret modeling tools.
• Communicate computational findings, model performance, and design recommendations to scientists and project teams and contribute to data-driven therapeutic development strategies.

Requirements:

• PhD in Computational Biology, Computational Chemistry, Machine Learning, Bioengineering, Chemical Engineering, Biomedical Engineering, or a closely related quantitative discipline, with at least 3 years of relevant industry experience.
• Demonstrated experience applying computational methods to protein, antibody, DNA, RNA, or oligonucleotide design, preferably within a drug discovery or biotechnology environment.
• Strong understanding of antibody engineering and computational approaches for analyzing antibody–antigen sequence, structure, binding, and interaction properties.
• Experience developing or applying advanced machine learning methodologies, including deep neural networks, transformers, graph-based models, protein language models, generative models, or related approaches.
• Hands-on experience using AI/ML to solve biological or molecular design problems, including predictive modeling, sequence analysis, structure prediction, or optimization.
• Knowledge of oligonucleotide therapeutics, ASOs, RNA biology, exon skipping, siRNA, PMO/gapmer chemistry, or related modalities is highly desirable.
• Strong programming capabilities in Python, with experience in one or more additional languages such as R or SQL.
• Proficiency with modern machine learning and scientific computing frameworks such as PyTorch, TensorFlow, scikit-learn, JAX, or comparable technologies.
• Experience working with large biological datasets and integrating sequence, structural, experimental, and literature-derived information for computational modeling.