Material Informatics Scientist

About us
R2A Labs is a young, rapidly growing startup working to break the dependence on fossil-derived materials that underpin so much of what we manufacture and use every day.
We are building technology to rethink bio-based material innovation and develop scalable solutions that compete with synthetic incumbents on both performance and cost. At the core of this is our R2A Lab-in-Loop Engine, a design system that replaces broad experimental trial-and-error with simulation-guided and highly targeted physical experimentation. By bridging the digital and physical worlds we develop high-performing and manufacturable bio-based materials, faster, and smarter.

About the role
We are looking for a Materials Informatics Scientist to sit at the centre of our discovery loop, converting chemical and physical modification strategies into digital workflows, and ensuring that simulation outputs and model behaviour translate faithfully into experimental decisions.

What you'll do

• Build and maintain molecular dynamics (MD) and DFT workflows that translate chemical and physical modification strategies into robust data pipelines
• Design and implement evaluation frameworks for computational models, defining metrics that capture generalisation, robustness, uncertainty, and experimental relevance, and assessing trade-offs between accuracy, data efficiency, and usability
• Close the loop between models and experiments: connect simulation outputs to validation results, identify and investigate discrepancies, and provide clear, actionable guidance to the lab team
• Develop robust analysis pipelines and benchmarking tools; visualise high-dimensional results in ways that surface real insight rather than confirm assumptions
• Interface closely with the experimental programme, identifying which data is most informative for model updates and ensuring learning compounds across iterations
• Actively shape data pipelines and analytical tools through hands-on use, ensuring infrastructure evolves in line with real scientific workflows

Your profile

• PhD (strongly preferred) or Master's (with 3–5 years post-degree experience) in computational chemistry, materials science, physics, or a closely related field with demonstrated experience in simulation-driven projects
• Hands-on experience with MD simulations combined with working knowledge of DFT workflows
• Strong Python skills and a track record of writing clean, modular, maintainable scientific code; experience with MD analysis libraries, and cheminformatics tooling (RDKit, pymatgen, ASE)
• Demonstrated ability to evaluate and benchmark models rigorously, with a deep appreciation for reproducibility and careful interpretation of results under uncertainty
• Clear scientific communication: able to write a summary that a lab scientist, an ML engineer, and a programme lead can all act on
• Strong builder mindset, motivated by solving hard, high-impact problems

Nice to have

• Experience with soft matter simulations
• Familiarity with: Gaussian process models, Bayesian Optimisation, or graph neural networks
• Exposure to machine-learned interatomic potential frameworks

Why join us?
You will join as a founding technical hire, with direct technical ownership and meaningful equity participation, helping shape a venture designed to bring high-performance materials with a fundamentally positive environmental impact to market.