About R2A 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 Software Engineer to help build the data infrastructure and software systems that power R2A’s design engine, connecting simulation, machine learning, and experimental workflows.
What you'll do Build and maintain scalable data pipelines that ingest and unify data from multiple computational and experimental sources, making it reliable and usable for downstream systems Develop and productionise ML workflows, including surrogate models and optimisation loops, with a focus on robustness, monitoring, and performance Design and extend data architectures that support workflows from early-stage research through to process optimisation and scale-up Build backend services and job orchestration systems to manage compute workloads, data processing, and model execution Improve system reliability by identifying gaps between model outputs and real-world results, and surfacing clear signals to guide iteration Develop internal tools, dashboards, and APIs that make data and model outputs accessible and actionable for the team Ensure best practices in code quality, testing, versioning, and reproducibility across the technical stack
Your profile Degree in Computer Science, Applied Mathematics, Physics, or a related quantitative field, with 2+ years of post-degree experience building and deploying data-intensive software systems (or PhD equivalent) Strong Python fundamentals and demonstrated experience building robust data pipelines across heterogeneous sources and formats Demonstrated data engineering or data science capability: designing and managing structured data flows across various sources, building pipelines that remain coherent as the system scales from early discovery through to process optimisation and scalability assessment Experience with probabilistic ML or Bayesian methods applied in research or production settings; solid PyTorch experience a strong plus Comfortable working across the stack: data pipelines, model training, basic infrastructure, and lightweight internal tooling; good judgment in low-data, noisy-signal environments Curious about physical systems and material science, motivated to develop the scientific intuition needed to build software that operates meaningfully inside a materials discovery and scale-up workflow Strong builder mindset, motivated by solving hard, high-impact problems
Nice to have Familiarity with molecular or materials ML: graph neural networks, equivariant architectures, or materials foundation models Experience with HPC environments and/or scientific computing infrastructure Experience with process modelling, digital twin frameworks, or engineering scale-up workflows
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.