AI and Computational Biology

About the Role

We are seeking a highly motivated and innovative Postdoctoral Fellow to join our interdisciplinary R&D team on a fixed-term fellowship. In this role, you will work closely with data scientists, software engineers, and AI engineers to build, train, and deploy foundation models, computational pipelines, and proprietary platforms for pharma and biotech applications. You will serve as the critical bridge between biological complexity and computational scale — translating biological problems into machine learning workflows and applying state-of-the-art generative AI to drug discovery, including small-molecule and biologics design, to advance our therapeutic pipeline. This fellowship is designed for recent PhDs looking to apply their research training to industry-scale model building, fine-tuning, and platform development.

Key Responsibilities

• AI/ML Pipeline Development: Collaborate with software and AI engineers to design, build, and scale end-to-end computational pipelines for de novo molecule design, target-interaction prediction, and PK/PD property prediction.
• Cross-Functional Collaboration: Work daily with data scientists, machine learning engineers, and wet-lab scientists to understand complex drug discovery challenges and translate them into actionable, data-driven computational strategies.
• Domain-Specific AI Application: Leverage, fine-tune, and evaluate state-of-the-art foundation models (e.g., AlphaFold2/3, Boltz-1/2, ESM-2/3, Evo, RFdiffusion, RFAntibody) to solve problems in protein engineering, target identification, and molecular optimization.
• Agentic & Generative Workflows: Help architect and deploy agentic AI workflows that autonomously query biological databases, propose molecular designs, and simulate experimental outcomes.
• Data Analysis & Curation: Analyze large-scale multi-omic, sequence, chemical, and structural datasets, and develop rigorous benchmarking and evaluation frameworks to identify where biological models succeed or fail.
• Closing the Loop: Perform retrospective analyses connecting in-vitro experimental outcomes (e.g., binding affinity, stability, developability) back to computational predictions to iteratively improve model performance.
• Platform Innovation: Contribute to the development of our internal proprietary AI platform, ensuring it remains robust, reproducible, and accessible to drug discovery teams and external partners.

Essential Expertise & Qualifications

• Education: PhD in Computational Biology, Life Sciences, Bioinformatics, Biophysics, Computer Science, Machine Learning, or a closely related quantitative discipline.
• Programming Skills: Strong proficiency in Python and standard scientific computing libraries (NumPy, SciPy, Pandas); comfortable writing clean, version-controlled, reproducible code.
• Communication: Exceptional ability to translate complex computational concepts for biologists, and biological constraints for software engineers and data scientists.

Preferred Qualifications

• AI/ML Experience: Demonstrated experience developing, implementing, or fine-tuning AI/ML-based solutions and computational pipelines in a life sciences context, whether from academic or industry work.
• Domain-Specific Models: Hands-on experience with modern generative AI/ML models tailored for biology, such as AlphaFold2/3, Boltz-1/2, ESM-2/3, and Evo.
• Deep Learning Frameworks: Familiarity with PyTorch or JAX and an understanding of deep learning architectures (Transformers, diffusion models, GNNs) applied to biological data.
• Biotech/Pharma Background: Prior experience in drug discovery or therapeutic design (antibodies, peptides, small molecules), or direct experience with biotech/pharma partners.
• Research Track Record: Peer-reviewed publications, preprints, or conference presentations demonstrating strong technical and problem-solving ability (a plus, not required).

What We Offer

• The opportunity to work at the cutting edge of AI and biology, driving real-world impact in therapeutic discovery.
• A collaborative, talent-dense team of world-class scientists and engineers.
• A competitive fellowship compensation package.
• A high-performance work environment with state-of-the-art computational resources, including GPU clusters.