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.