Computational Biologist

Join to apply for the Computational Biologist role at Epoch Biodesign

Epoch Biodesign is a well-funded, venture-backed start-up using biology to make every type of plastic recyclable - starting with nylon.

Using a unique combination of AI, synthetic biology and green chemistry, we are scaling enzymatic recycling in order to transform currently unrecyclable plastics and textiles into new, virgin-quality materials. Our technology yields substantial reductions in carbon emissions with disruptive unit economics, preventing waste from entering landfill or the environment, allowing us to solve this very urgent challenge.

With our pilot plant already processing nylon 6,6 waste at the multi-tonne level, we will imminently complete construction on our larger demo facility. This site will produce material destined for use in garments made by some of the world’s biggest fashion, sportswear and luxury brands, and also in components for some of the world’s largest car companies.

As a Computational Biologist at Epoch, your core function will be to: to support enzyme design and screening cycles by delivering practical computational protein design tools and analyses. The successful candidate will be able to translate well‑scoped questions into reproducible analyses and pipelines, and will be comfortable iterating rapidly as new data becomes available. Full domain coverage is not expected on day one;

a strong foundation, delivery focus, and willingness to learn are essential.

• Develop, improve and maintain computational pipelines for both protein design (discovery) and analysis of subsequent high‑throughput screening data (optimisation)
• Analyse high‑throughput screening/assay data and communicate conclusions in a form that directly informs experimental decision‑making, and delivers insights to guide future iterations of protein libraries
• Rapidly prototype minimally viable analyses and tools, and subsequently transition successful solutions into robust, maintainable production code
• Work closely with experimental stakeholders to clarify requirements, identify sources of assay variability/artifacts, and iterate on analyses based on laboratory feedback
• Conduct targeted literature and method reviews to support tool selection and analytical approaches, ensuring relevance and currency
• Maintain high‑standard documentation and data frameworks that facilitate internal knowledge transfer and, when required, can be used to support patent filings, grant applications and publications

• PhD (or equivalent research experience) in a relevant field with a strong experimental component (biochemistry, molecular biology, biotechnology, chemical biology, bioengineering, or related). We encourage applications from candidates who have recently completed their PhD and are looking to transition to an innovative and collaborative start‑up environment
• Meaningful hands‑on laboratory experience relevant to protein/enzyme engineering (e.g., cloning/expression/purification and/or assay development/high‑throughput screening), including ability to interpret results within a design–build–test–learn workflow
• Demonstrated computational delivery applied to experimental datasets, including strong working knowledge of Python and common scientific libraries (e.g., Num Py, Pandas, Sci Py; visualisation with Matplotlib/Plotly or equivalent), and evidence of reproducible end‑to‑end analyses/pipelines (scripts, tools, or documented notebooks)
• Hands‑on experience with common protein engineering and structural visualisation tools, including PyMOL and Rosetta, with demonstrated prior use in protein structure analysis and/or design workflows
• Evidence of scientific output relevant to protein engineering or computational biology (e.g., publications, preprints, datasets, internal tools, or open‑source contributions)
• Ability to work independently on well‑scoped tasks, including communicating risks early and seeking input when appropriate

• Experience with version control (git) and Linux/Unix environments
• Working understanding of one or more of: bioinformatics/NGS data analysis,…