GSK is looking to solve the business problem of accelerating the discovery of new medicines and vaccines by developing, integrating, and embedding cutting-edge computational methods and predictive in silico models within their R&D processes. The goal is to become the predictive engine for R&D, enabling the automation of the entire Design-Make-Test-Analyze cycle and driving Lab-in-an-Automated-Loop frameworks from target discovery to the clinic.
Requirements
- Experience in protein structural or sequence analysis
- Experience in one or more programming languages (e.g. Python)
- 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
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.
Other
- PhD or equivalent in Bioinformatics, Physics, Chemistry, Computer Science, Structural Biology, or related fields
- 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
- 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
