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Computational Protein Design Scientist
An exciting opportunity has arisen to join a recently funded, early-stage biotechnology company developing a highly ambitious programmable therapeutics platform for oncology.
This fast-moving biotech is building a new class of target-selective therapeutic systems, designed to act only in disease-relevant cellular contexts. The company’s platform integrates:
- Design and engineering of proteins with controllable behaviour
- Advances in protein structure prediction (e.g., AlphaFold, RoseTTAFold)
- Generative design and protein language models
- Experimental validation
Having secured seed funding, the company is expanding its technical team and seeking a Computational Protein Design Scientist to establish its AI-enabled protein engineering capability. Ideal candidates will thrive at the intersection of protein design, structural biology, computational modelling, and wet-lab validation.
Why Join?
✔ Join a seed-funded biotech during a crucial platform and pipeline development phase. ✔ Help define the company’s computational protein design strategy from an early stage. ✔ Work on highly novel protein systems where structure, function, activation, and selectivity are pivotal for therapeutic effectiveness. ✔ Apply cutting-edge structure AI tools to solve real-world design challenges. ✔ Close the loop between computational predictions and experimental validation, driving hypotheses into biological data.
Job Title: Computational Protein Design Scientist Location: Remote or London (Hybrid) Reports To: CTO Salary: Competitive, based on experience
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About The Role
As a Computational Protein Design Scientist, you will leverage AI-driven protein structure prediction, design tools, and modelling to engineer and optimise engineered therapeutic proteins. This role sits at the forefront of AI-driven protein engineering, bridging computations with experimental validation.
Key Responsibilities
- Optimise and evaluate engineered proteins using:
- Protein structure tools (AlphaFold, Boltz, RoseTTAFold, Protenix)
- Protein design and modelling platforms (Rosetta, BindCraft, ProteinMPNN, ESM)
- Design and prioritise variants for experimental testing, considering structure, function, activation, and selectivity.
- Build computational workflows for:
- In silico construct design
- Structural validation (folding, binding, interface)
- Binder and interaction design
- Protein optimisation
- Collaborate closely with wet-lab teams to translate computational insights into testable hypotheses.
- Assess model outputs critically—balancing confidence metrics, technical pitfalls, and experimental feasibility.
- Shape engineered activation systems, target-selective mechanisms, and controllable protein behaviours.
- Integrate computational predictions with experimental data to refine future iterations.
- Develop/maintain pipelines for pipeline reuse, analysis, and infrastructure.
- Stay ahead of advancements in protein design and structure AI, identifying innovative applications for the platform.
Qualifications & Experience
Desired
- PhD (or equivalent) in:
- Computational Protein Design
- Structural Biology
- Computational Biology
- Protein Engineering
- Biophysics
- Bioinformatics (relevant fields)
- Hands-on experience with:
- Protein structure prediction (AlphaFold, Boltz, RoseTTAFold)
- Design platforms (Rosetta, BindCraft, ProteinMPNN, ESM)
- Understanding protein folding, binding interfaces, conformational flexibility
- Generative AI or language models for protein design
- Track record of:
- Designing/evaluating engineered proteins (binders, enzymes, switches, scaffolds)
- Bridging computations → experiment through iterative structural validation
- Technical skills:
- Python, reproducible computational workflows
- Bioinformatic tools (FASTA, PDB, MD analysis, visualisation)
- Strong sense of:
- Model biases (confidence scores, artefacts, hallucinations)
- Cancer cell biology, targeted therapeutics, selective interactions
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Preferred
- ** CRISPR/Cas systems**, programmable biology, scaffold redesign
- Integration of structural methods (cryo-EM, X-ray) with AI models
- Molecular dynamics or biophysical simulations for validation
- Optimization workflows on high-performance clusters/cloud infrastructure
- Entrenchment in oncology, cell-selective systems, or synthetic biotechnologies
Exciting Opportunity
This is a foundational role for scientists who want to shape: ✅ An early-stage biotech’s computational engine ✅ Next-gen therapeutics via AI-guided protein design ✅ A new class of targeted oncology treatments
Application: Submit an updated CV for consideration.
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