Protein Engineering and Innovation Scientist Full-time

Protein Engineering Lead

This role will lead the development and deployment of state-of-the-art in-silico protein engineering and design principles for generation of complex reagents to progress the discovery portfolio. You will work collaboratively with computational experts and protein expression teams to design, generate, and test engineered proteins and to aid discovery of novel medicines.

You will work closely with discovery project teams, computational scientists, and external partners, contributing to both scientific innovation and project delivery.

This position offers strong visibility, opportunities for learning, and the chance to help shape next-generation protein engineering capabilities within GSK.

This is an opportunity to play a key role in advancing molecular design and engineering capabilities within LMR, supporting complex targets and next-generation modalities.

The role contributes directly to critical portfolio delivery and emerging technology platforms, including AI/ML-enabled design approaches.

We create a place where people can grow, be their best, be safe, and feel welcome, valued and included. We offer a competitive salary, an annual bonus based on company performance, healthcare and wellbeing programmes, pension plan membership, and shares and savings programme.

We embrace modern work practices; our Performance with Choice programme offers a hybrid working model, empowering you to find the optimal balance between remote and in-office work.

Discover more about our company wide benefits and life at GSK on our webpage Life at GSK | GSK

In this role you will:

• Carry out construct design and protein engineering for the generation of complex proteins and panels of diverse protein classes. Protein classes to include, multi subunit, multidomain and membrane proteins.
• Develop and deploy in silico Molecular Biology and Protein Design tools.
• Develop and execute experimental workflows that connect Molecular Biology design with laboratory automation.
• Collaborate with computational design, structural biology, and analytical teams to translate insights into experimental construct and protein design strategies.
• Develop approaches for external and internal database searching, for protein reagents and constructs.
• Build external collaborations and evaluate emerging technologies or vendors to enhance protein engineering capabilities.
• Develop workflows that enable data use and re-use, and which maintain a high standard of data integrity.
• Manage timelines, communicate results, and provide clear scientific recommendations to cross-functional teams.

Why you?

Basic Qualifications & Skills:

• PhD in Biochemistry, Protein Engineering, Molecular Biology, or a related discipline and significant industry experience.
• Proven track record of construct and molecule design for the generation of complex antigens and panels of proteins of different classes; to include multi subunit and multidomain and membrane proteins.
• Demonstrated skills in experimental cloning and expression techniques for a range of different protein classes including multi subunit and multidomain and membrane proteins.
• Practical experience with high throughput expression systems and the use of automation.
• Proven track record of use of construct design tools (e.g. Snapgene, Genius Prime, VectorBuilder or similar).
• Proficiency in programming, data analysis and visualization tools and proven track record of using these skills for project delivery (e.g. Python, R or similar).
• Data skills and experience in manipulation of large volumes of data, and tools to enable efficient data analysis and visualisation (eg Spotfire or similar).
• Proven track record of working in partnership with computational scientists and working collaboratively across multidisciplinary teams.
• Evidence of knowledge sharing and training skills.

Preferred Qualifications & Skills:

• Deep experience in bioinformatics, including tools to identify relevant orthologues, homologues, isoforms, and variants to support protein engineering strategies (e.g. Blast Searches).
• Practical experience in building and utilising workflows enabling efficient data re-use.
• Proven track record of use of protein design tools (e.g., Pymol, MOE, alpha fold or similar).
• Evidence of active engagement in external landscape and introduction of novel technologies.