Cubiq Recruitment
Bioinformatics Scientist
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Bioinformatics Scientist (Microbial Genomics)
London (On-site 4-5 days) | Up to £70,000 + Equity | Some flexibility for exceptional candidates
We're working with a London-based BioAI company that holds one of the largest proprietary biodiversity datasets in the world. Backed by a major AI infrastructure company and fresh off a Series B, they've sampled environments across the globe and are sitting on billions of genes, a huge proportion of which are microbial, novel, and largely uncharacterised.
This is frontier biology. They're mapping genetic data across 100M+ species with the goal of expanding known biology by orders of magnitude, and training foundation models on data that simply doesn't exist anywhere else.
The Role
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You'll be working with large-scale microbial and metagenomic data that is messy, novel, and full of potential. The expectation is that you develop new methods and tools to make sense of it, not apply existing ones. You'll be building things, publishing things, and directly shaping how one of the richest biodiversity datasets in the world gets analysed and understood.
Requirements
- You have built a bioinformatics tool, method, or software package that other people can use. Published, open-sourced, or otherwise demonstrable. This is the single most important requirement. The role is fundamentally about developing new methods, so a track record of building is essential.
- Experience working with large-scale microbial or metagenomic data
- Strong programming skills (Python, R, or similar) oriented toward building software, not just writing analysis scripts
- Comfortable working with messy, real-world biological data that requires developing new approaches
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Preferences
- Published tool or method in a peer-reviewed journal, or available on GitHub/PyPI/Bioconda
- Experience with pangenomics, metagenome binning, microbial community analysis, or functional annotation
- Familiarity with workflow managers (Nextflow, Snakemake) for deploying your own tools at scale
- Exposure to machine learning applied to biological data
This role is on-site in London 4-5 days per week. International relocation support is available.
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