Principal Scientist, Molecular Perturbation Modeling

Role Summary

Principal Scientist, Molecular Perturbation Modeling. Translate biological mechanisms of disease to molecular mechanisms of therapeutics by integrating perturbation data to design new molecules that modulate disease phenotypes. Focus on de novo design technologies to generate molecules for testing hypotheses at scale and accelerate therapeutic discovery within the Data, Automation, and Predictive Sciences (DAPS) department.

Responsibilities

• Generate, validate, and integrate multimodal generative AIML models for de novo design and multi-objective optimization of tool and therapeutic molecules (e.g., miniproteins, antibodies, antigens, peptides, ADCs, oligonucleotides).
• Guide molecular perturbation experiments that validate disease mechanisms and show reversal of disease phenotypes and signatures.
• Build and exploit integrated Design-Make-Test-Analyze cycles with automated experimental platforms, generating high-quality data at scale for project-specific and foundational models.
• Identify opportunities in scientific computation and platform automation, driving therapeutic project plans with predictive technologies.
• Collaborate with external groups to 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

• Required: PhD or equivalent in Bioinformatics, Physics, Chemistry, Computer Science, Structural Biology, or related fields.
• Required: Experience in protein structural or sequence analysis.
• Required: Experience in one or more programming languages (e.g., Python).
• Required: Experience with training or applying multimodal input (sequence, structure, genetic, small/large molecular, etc.) and output (imaging, omics, etc.) ML models.
• Required: Experience to work as team lead or member; ability to work/lead effectively in a matrix environment.
• Required: Experience working across scientific and technical disciplines to deliver impactful solutions that drive project progression.

Skills

• Preferred: Experience developing or applying modern ML architectures for molecular design models (LLMs, diffusion models, flow-matching, Bayesian Optimization, GNNs, etc.).
• Preferred: Experience with design of multiple therapeutic modalities.
• Preferred: Experience designing de novo binders for specified targets and epitopes to answer biological questions.
• Preferred: Experience with cloud engineering production-ready scalable scientific workflows.
• Preferred: Experience building and deploying agentic workflows.
• Preferred: Learning agility and scientific curiosity with focus on impact under uncertainty.
• Preferred: Ability to generate conclusion reports, present data in team meetings, and contribute to abstracts/publications.

Education

• PhD or equivalent as listed in Qualifications.