Research Scientist, Biologics

Help bring science to life and join a leading biotech company where you will play a key role in delivering impactful research that drives healthcare innovation!

Proclinical is seeking a Research Scientist specializing in AI and Machine Learning for biologics. In this role, you will contribute to the development of advanced computational frameworks.

This position will be required to support the design and optimization of antisense oligonucleotides (ASOs) and biologics. You will play a key role in integrating machine learning and artificial intelligence into therapeutic discovery processes, helping to accelerate innovation across multiple modalities.

• Advanced degree (PhD preferred) in Computational Chemistry, Biology, Machine Learning, Biomedical/Chemical Engineering, or a related field.
• Strong background in oligonucleotide chemistry and antibody design/characterization.
• Proven experience in computational modeling of antibody-antigen interactions, including sequence and structure analysis.
• Expertise in probabilistic learning, deep learning models (e.g., RNNs, GNNs, Transformers), and generative AI.
• Proficiency in programming languages such as Python, R, and SQL, with experience using frameworks like PyTorch, Tensor Flow, or scikit-learn.
• Experience developing machine learning models for DNA, RNA, and proteins, including language models and structure prediction.
• Familiarity with large-scale computing, cloud infrastructures, and database systems (e.g., AWS, Git Hub, Docker).
• Strong communication and collaboration skills to work effectively with multidisciplinary teams.
• Commitment to continuous learning and a team-oriented mindset.

• Design and implement advanced AI/ML approaches for antibody discovery, including fine-tuning protein language models and creating generative protein design workflows.
• Develop and scale machine learning methods for optimizing antibodies, antigens, ADCs, and other biologic modalities.
• Build predictive models to prioritize ASO designs based on exon-skipping response across various targets and modalities.
• Create reproducible computational frameworks for biologics, including data ingestion, feature engineering, model training, validation, and deployment.
• Curate and harmonize datasets from internal and external sources, defining robust sequence and structure features to enhance model performance.
• Establish benchmarks and conduct prospective tests to assess model accuracy, robustness, and scalability. Collaborate with experimental teams to validate predictions.
• Evaluate and adopt proprietary and open-source tools to improve modeling workflows and decision-making processes.
• Maintain a clean, well-documented codebase and provide user guidance for cross-functional teams.
• Perform additional related tasks as needed.

• $60 to $73 per hour.