Research Scientist, AI /ML Biologics Job at Beacon Hill (Cambridge)

Job Description

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. 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.

Job Responsibility

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

Requirements

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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Research Scientist, AI /ML Biologics

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