AI Scientist / Senior AI Scientist — Protein Design & Engineering

What you’ll work on

• Design and screen variants of therapeutic proteins, antibodies, peptides, fusion proteins and other large molecules. Optimize activity, binding affinity, selectivity, stability, solubility, expression, pharmacokinetics and overall developability, while reducing aggregation tendency.
• Apply AI, machine learning and structure-based computation to lead discovery and optimization, using protein language models, generative design, structure prediction, inverse folding, sequence design and molecular modelling.
• Build reproducible, verifiable workflows for sequence design, structural assessment, multi-parameter optimization and candidate prioritization.
• Combine internal experimental data with public protein sequence, structure, function and developability data to support model development and project decisions.
• Help establish iterative design–build–test–learn workflows with experimental teams. Analyze positive and negative results to identify why designs succeed or fail, then refine molecular designs and models.
• Evaluate emerging AI and computational protein-design methods, including their limits and value for specific projects. Turn drug-discovery needs into questions that can be addressed computationally and tested experimentally.
• Help develop the protein-design platform, internal data standards and model-evaluation methods. Support decisions across competing molecular properties, with progress of candidates and development programmes as the goal.
• Contribute to patent applications, papers, external collaborations and scientific exchange as projects require.

What you’ll bring

• A PhD in computational biology, bioinformatics, structural biology, biophysics, protein engineering, computer science, machine learning or a related field.
• Practical experience applying AI or computational methods to protein design or engineering, antibody engineering, peptide design or large-molecule drug discovery.
• A sound understanding of the relationship between protein sequence, structure and function.
• Proficiency in Python and familiarity with scientific-computing, machine-learning or deep-learning tools.
• The ability to interpret biochemical, biophysical, cellular or pharmacological data and turn experimental findings into actionable molecular designs.
• Good scientific judgment, analytical and communication skills, and the ability to work with computational, experimental and project colleagues.

Relevant research experience

You should have research or project experience in one or more of these areas:

• Protein structure prediction and modelling.
• Protein language models or generative protein design.
• Inverse folding, sequence design or sequence optimization.
• Antibody modelling or analysis of protein–protein interactions.
• Molecular docking or molecular-dynamics simulation.
• Multi-parameter protein optimization.
• Machine-learning analysis of biological and experimental data.

Preferred experience

• Work in biotechnology, pharmaceuticals or a protein-design laboratory focused on translation into practical applications.
• Optimization of therapeutic antibodies, Fc fusion proteins, cytokines, enzymes, peptides or other large-molecule medicines.
• Familiarity with AlphaFold, Rosetta, ProteinMPNN, RFdiffusion, protein language models or similar tools and platforms.
• Projects that combine computational design with experimental validation through repeated design and testing.
• Experience in affinity maturation, specificity optimization, pH-dependent binding, half-life extension, stability improvement, aggregation-risk reduction or immunogenicity-risk assessment.
• Knowledge of requirements in large-molecule developability, pharmacokinetics, translational pharmacology, CMC or biologics manufacturing.
• Model training, fine-tuning or active learning using internal experimental data.
• Experience with GPU computing, high-performance computing, workflow automation or experimental-data integration.
• High-quality publications, patents or demonstrated results from drug-development projects.

Projects and research setting

You’ll contribute directly to large-molecule drug discovery and optimization, work with protein engineering, biology, pharmacology, CMC and clinical-development teams, and help build Innogen’s AI protein-design platform and proprietary data resources.

The research setting emphasizes translation into drug development, new approaches and practical results. Compensation and benefits reflect experience and skills.