Contract AI/ML Scientist (Antibody Design and Discovery)

Fully Remote/Contract Opportunity

Responsibilities of the AI/ML Scientist (Antibody Design and Discovery):

• 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.
• 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.
• Develop and extend sequenceaware machine learning models to prioritize ASO designs by predicted exonskipping response across multiple targets and modalities, including PMOgapmers, siRNA–PMO hybrids, and conjugate designs.
• Establish benchmarks and prospective tests to assess accuracy, robustness, and scalability, and partner with experimental teams to validate predictions.
• Evaluate and adopt proprietary and opensource 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 AI/ML Scientist (Antibody Design and Discovery):

• 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 ASO, siRNA, and antibody discovery.
• 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.