
Biographica helps agricultural and biotech teams find genes to edit faster, reducing time and failure in crop trait development. It uses graph-based machine learning and genomics to model biological…

Biographica helps agricultural and biotech teams find genes to edit faster, reducing time and failure in crop trait development. It uses graph-based machine learning and genomics to model biological…
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About Us 🌽 Safeguarding the future of food
Biographica is on a mission to accelerate the development of more productive, sustainable, nutritious & climate-resilient food sources. To achieve this, we're building the world’s first ML-driven target discovery platform for crop gene-editing.
🧬 Target discovery for gene-editing
While gene-editing of crops is becoming ever more efficient, identifying which genes to edit and how remains a significant challenge. To overcome this bottleneck, we use cutting-edge deep learning to accurately and efficiently identify high value genetic targets for crop gene-editing. Our approach draws inspiration from advancements in the drug discovery space, incorporating transformers, graphs & causal-ML to build a best-in-class discovery platform for plant sciences.
👥 Team
Led by co-founders Dom (CTO) and Cecy (CEO), we are now a team of 17. We primarily work together in person from our office in Spitalfields, London. We work 4 days per week in-person with fridays WFH. This role will be based in London, with close collaboration across ML, data and scientific product.
What we're looking for
As part of the Computational Biology team, you will lead on the development of robust, scalable omics workflows that power our discovery platform, with an initial focus on plant pangenomes. You will review and evolve our existing pipelines, define how pangenomes should strengthen discovery projects, and build the computational foundations that connect public and internal omics data to downstream ML and target discovery. Working across data, ML and plant science, you will own pangenome creation and curation, drive innovation in workflows spanning gene expression and variant calling, and contribute to scientific software that is reproducible, efficient and production-ready.
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