Senior Scientist​/Principal Scientist AI​/ML

About Maxion

Maxion Therapeutics is a biotechnology company developing antibody-based drugs for previously untreatable ion channel- and G protein-coupled receptor (GPCR)-driven diseases, including autoimmune conditions, chronic pain, and cardiovascular disease.

Maxion is developing a pipeline of potentially first- and best-in-class therapeutics using its proprietary Knot Body® technology to generate potent, selective, and long-acting therapeutics by combining naturally occurring mini-proteins (‘knottins’) with antibodies using state-of-the-art phage and mammalian display technologies.

Maxion was founded in 2020 by Dr John McCafferty (CTO) and Dr Aneesh Karatt-Vellatt (CSO). Dr McCafferty previously co-invented antibody phage display, which was the subject of the 2018 Nobel Prize in Chemistry awarded to his co-inventor Sir Gregory Winter. The Company is based near Cambridge, UK and is backed by international blue-chip investors. For more information, please visit:

We are seeking a highly skilled Senior AI Research Scientist with expertise in computational protein design and generative protein modelling to enable AI- and structure-guided approaches to therapeutic antibody and Knot Body design.

The successful candidate will drive the development, implementation, deployment and adoption of generative AI/ML models to enable therapeutic protein design, engineering and optimisation, utilising Maxion’s proprietary Knot Body technology.

This is a unique opportunity for someone who is excited to roll up their sleeves, build new capabilities from the ground up, and drive forward discovery programmes.

The successful candidate will bring strong technical skills, a collaborative mindset, and the ability to thrive in a fast-paced biotech environment.

• Develop the computational protein design platform through integration, adaptation and benchmarking of generative protein design & engineering tools (Alpha Fold/Open Fold, RF Diffusion, Protein
  
  MPNN, Boltz, Frame Flow, etc) into the drug discovery process.
• Build generative and predictive models for protein design by training and fine-tuning ML models (VAEs, diffusion models, transformers) focused on prediction of functional therapeutic proteins and their properties (affinity, stability, and develop ability).
• Enable computational optimisation of therapeutic proteins, leveraging various ML approaches (genetic algorithms, Bayesian optimisation, physics-based methods, etc.) and integrating experimental data.
• Build datasets, data pipelines, training workflows, and evaluation tools for model training, benchmarking, and continuous learning.
• Cross functional collaboration with internal R&D and discovery teams to translate predictive models into deployable tools and testable experimental hypotheses.

• Ph.D. or MSc. in Computational Biology, Computer Science, Bioinformatics, Natural Sciences or a related subject.

• Strong programming skills in Python and experience with deep learning frameworks (PyTorch, JAX, Tensor Flow in order of preference).
• Substantial experience of structural bioinformatics and computational protein design, for example: protein structure modelling & prediction, generative protein sequence & structure design, protein-protein docking, physics-based modelling & simulation, etc.
• Experience training and fine-tuning ML models for protein design or related tasks.
• Experience of integrating computational predictions with experimental validation data for property optimisation.
• Experience working with modern MLOps stacks (Docker, Kubernetes, CI/CD, Git Hub, etc.) to deploy and monitor models.
• Experience working with antibody sequence and structure datasets, using in silico tools for predicting protein properties and guiding engineering campaigns.

• Publication(s) in relevant peer-reviewed journals, ideally focused on antibody design, AI/ML based protein modelling, or non-standard scaffolds (e.g. knottins, mini binders, etc.).
• Experience applying generative or structure-based models to challenging target classes (e.g. ion channels, GPCRs).

• A competitive salary…