Principal Scientist, Biologic AI Research and Design

Role Summary

As a (Senior) Principal Scientist in the Protein Design and Informatics (PDI) team, you will focus on developing, integrating, and embedding bleeding edge computational methods and predictive in silico models that drive the discovery of new medicines and vaccines. You will be the predictive engine for R&D, focusing on researching and embedding new methods to enable automation of the Design-Make-Test-Analyze cycle, driving Lab-in-an-Automated-Loop frameworks from target discovery to the clinic.

Responsibilities

• Work to generate, validate, and integrate multimodal generative AIML models for the de novo design and multi-objective optimization of tool and therapeutic biologics, such as miniproteins, antibodies, antigens, peptides, ADCs, and oligonucleotides.
• Build and exploit agent-orchestrated, integrated Design-Make-Test-Analyze cycles with automated experimental platforms, generating quality data at scale needed for project-specific and foundational models.
• Innovate, develop, and apply predictive models for protein design and developability engineering, utilizing large-scale NGS, patient-derived, and other proprietary in-house and external data sources.
• Identify and advocate for the opportunities afforded by scientific computation and platform automation and driving therapeutic project plans with predictive technologies.
• Collaborate with external groups to further develop protein engineering computational methods.
• Predict and evaluate potential disease intervention points for their probability of success to be therapeutically modulated across any modality.

Qualifications

• PhD or equivalent in Bioinformatics, Physics, Chemistry, Computer Science, Structural Biology, or related fields
• Experience in protein structural or sequence analysis
• Experience in one or more programming languages (e.g. Python)
• Experience in working as team lead or member; ability to work/lead effectively in a matrix environment
• Having experience working across scientific and technical disciplines to deliver impactful solutions that drive project progression

Preferred Qualifications

• Experience developing or applying modern ML architectures for protein design models (LLMs, diffusion models, flow-matching, Bayesian Optimization, GNNs, etc.)
• Experience with training or applying multimodal input (sequence, structure, small and large molecular representations, etc.) and output (imaging, omics, etc.) foundational models
• Experience designing de novo binders for specified targets and epitopes to answer biological questions
• Experience with cloud engineering production-ready robust and scalable scientific workflows
• Experience building and deploying agentic workflows
• Experience analyzing NGS-derived antibody repertoires for sequence- and structure-based design
• Experience predicting structures of RNA or nucleic acid-protein complexes
• Experience with molecular simulation and conformational analysis techniques
• Demonstrated learning agility, and scientific curiosity while maintaining focus on driving greater impact in the face of uncertainty and change
• Ability to generate conclusion reports, present data in team meetings and participate in writing of abstracts and publications for the scientific community

Education

• PhD or equivalent in a relevant field as listed in Qualifications