Principal Scientist, Molecular Perturbation Modeling

Role Summary

As a (Senior) Principal Scientist in the Protein Design and Informatics (PDI) team, you will focus on translating biological mechanisms of disease to molecular mechanisms of therapeutics by integrating perturbation data to design new molecules that modulate disease phenotypes. The role includes advancing de novo design technologies to generate molecules to test hypotheses at scale, building data to predict perturbation effects, accelerating validation of intervention points, and driving therapeutic discovery campaigns within the Data, Automation, and Predictive Sciences (DAPS) department. You will be the predictive engine for R&D, researching and embedding new methods to automate the Design-Make-Test-Analyze cycle, driving Lab-in-an-Automated-Loop frameworks from target discovery to the clinic across all stages of a therapeutic project. You will collaborate across GSK to foster a high-performing, collaborative, curious, and quality-driven team focused on creating medicines for patients.

Responsibilities

• Work to generate, validate, and integrate multimodal generative AIML models for the de novo design and multi-objective optimization of tool and therapeutic molecules, such as miniproteins, antibodies, antigens, peptides, ADCs, and oligonucleotides.
• Guide molecular perturbation experiments that validate mechanisms of disease and show reversal of disease phenotypes and signatures.
• 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.
• 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 with training or applying multimodal input (sequence, structure, genetic, small/large molecular, etc.) and output (imaging, omics, etc.) ML models
• Experience to work as team lead or member; ability to work/lead effectively in a matrix environment
• Experience working across scientific and technical disciplines to deliver impactful solutions that drive project progression

Skills

• Experience developing or applying modern ML architectures for molecular design models (LLMs, diffusion models, flow-matching, Bayesian Optimization, GNNs, etc.)
• Experience with the design of multiple therapeutic modalities
• 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
• 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)