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The Importance of the Role
Our client is expanding its capabilities in antisense oligonucleotide (ASO) therapeutics while advancing antibody and biologics discovery across multiple targets. To support this effort, we are integrating advanced machine learning (ML), artificial intelligence (AI), and data driven modeling to accelerate discovery and optimization across modalities. The Research Scientist, AI / ML Biologics will help build reproducible computational frameworks that strengthen their AI/ML assisted design pipelines for ASOs and emerging biologic program.
The Opportunity to Make a Difference
In this role, you will develop sequence aware predictive models to prioritize oligonucleotide therapeutics based on exon skipping response and extend these approaches across diverse ASO modalities. You will also contribute to AI/ML driven antibody discovery by supporting modeling of antibody-antigen sequence, structure, and interactions, helping expand the client’s cross modality design and decision support capabilities.
Key Responsibilities
- Design and implement advanced AI/ML approaches for de novo antibody discovery, including finetuning protein language models and developing generative protein design workflows.
- Develop, deploy, and scale machine learning methods to support multi objective optimization of antibodies, antigens, ADCs, and other biologic modalities.
- Develop and extend sequence aware machine learning models to prioritize ASO designs by predicted exon skipping response across multiple targets and modalities, including PMOgapmers, siRNA-PMO hybrids, and conjugate designs.
- Build a reproducible computational framework including data ingestion, feature engineering, model training, validation, and deployment for biologics.
- Curate and harmonize internal and external literature curated datasets and define robust sequence and structure features such as thermodynamics, accessibility, sequence motifs, secondary structure, and additional context that drive model performance.
- Establish benchmarks and prospective tests to assess accuracy, robustness, and scalability, and partner with experimental teams to validate predictions.
- Evaluate and adopt proprietary and open source tools to enhance modeling workflows and accelerate decision support.
- Maintain a clean and well documented codebase, and user guidance for cross functional teams.
- Perform additional related tasks as assigned
Required Skills & Education
- PhD in Computational Chemistry/Biology, Machine Learning, Biomedical/Chemical Engineering field with 3+ years of directly relevant industry experience.
- A strong background in oligonucleotide chemistry, antibody design and characterization.
- Proven experience in computational modeling of antibody-antigen sequence, structure, and binding interactions.
- Strong expertise in developing and/or adopting probabilistic learning or deep learning models, including Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), Transformers, Natural Language Processing models, and Generative AI.
- Expert in programming and scripting languages such as Python, R, and SQL, with hands-on experience using modern deep learning frameworks such as PyTorch, Tensorflow, skikit-learn or JAX.
- Experience in developing machine learning models for DNA, RNA, and proteins, including language models, structure prediction, and design.
- Familiar working with large-scale computing and cloud infrastructures, database systems, and development tools in a production environment.
- Familiar with data development, tools, and infrastructure: AWS, database technologies, GitHub, GitLab, and Docker containers
- Ability to effectively communicate and collaborate with a multidisciplinary team, including chemists, biologists, and data scientists to successfully complete scientific projects.
- Strong team player with a commitment to continuous learning.
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