Contract, Research Scientist, AI /ML Biologics

Join a Leading Biotech Company

Become a vital part of an innovative biotech organization, where you will help advance healthcare through impactful research. We are looking for an individual with a strong background in AI and Machine Learning applied to biologics.

Key Responsibilities

• Support the design and optimization of antisense oligonucleotides (ASOs) and biologics.
• Integrate machine learning and artificial intelligence into therapeutic discovery processes to accelerate innovation across various modalities.
• Design and implement advanced AI/ML approaches for antibody discovery, including fine-tuning protein language models.
• 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.
• 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, enhancing model performance through robust sequencing and structure features.
• Establish benchmarks and conduct tests to assess model accuracy, robustness, and scalability, collaborating with experimental teams to validate predictions.
• Evaluate and adopt 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 required.

Skills & Requirements

• 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.
• 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, and experience with frameworks like PyTorch, TensorFlow, or scikit-learn.
• Experience in 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, GitHub, Docker).
• Strong communication and collaboration skills to work effectively with multidisciplinary teams.
• Commitment to continuous learning and a team-oriented mindset.

Compensation

Compensation for this role ranges from $60 to $73 per hour.