Senior Multi-Omics Microbiologist for AMR

About us

Spore.Bio is a deeptech startup founded in 2023 that is redefining microbiological quality control in pharmaceutical, food and beverage, and cosmetics manufacturing. Where Spore.Bio deploys biophotonic and deep-learning technology on factory floors, Spore.Labs takes Spore.Bio's core technology into new territory: from AMR detection to microbiome research and beyond.

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We are building a high-impact interdisciplinary team at the intersection of biophotonics, machine learning, and microbiology, with a shared mission: to develop a breakthrough technology combining biophotonics and artificial intelligence to revolutionise the diagnosis of antibiotic-resistant infections. This project aims to create a novel approach capable of rapidly identifying bacterial resistance, persistence, and tolerance mechanisms through the integration of optical, biological, and computational data.

We are building our microbiology laboratory from the ground up and are looking for someone ready to take real ownership, shape how we work, and grow alongside the department they help build.

About the role

This is not a supporting position. You will own the scientific interpretation of multi-omics data for our AMR platform, working from raw genomic, transcriptomic, and proteomic or metabolomic data through to biological insight that directly shapes what our models learn and how we validate what they find. Your expertise will guide model development and interpretation within a highly collaborative, multidisciplinary environment, working as a scientific peer to our deep learning team rather than a data preparation function upstream of it.

Main Responsibilities

• Multi-omics characterisation of resistanceYou will work across genomics, transcriptomics, and at least one of proteomics or metabolomics to build a mechanistic picture of resistance, persistence, and tolerance in our strain library and clinical isolates. This includes bringing a working understanding of bacterial population structure, evolutionary dynamics, and how resistance spreads and is maintained, to bear on how we design experiments and interpret their output.

• Genotype-phenotype linkage You will work with paired genomic and quantitative susceptibility data (AST, MIC) to characterise where sequence-based prediction holds and where it diverges from measured phenotype. You will bring resistome literacy at a mechanistic level, not just annotation-level familiarity, and will be the person in the room who can explain why a genotypic call and a phenotypic result disagree.

• Data curation for model-ready pipelines You will interrogate, curate, and structure complex, heterogeneous, and often clinically-derived biological data (underrepresented sequences, mixed populations, ambiguous or noisy signal) so that it is usable for training pipelines without losing scientific rigour along the way. You will apply alignment-free and reference-free methods where standard approaches fall short, and you will know the failure modes of building generalisable models from real-world biological data well enough to flag them before they surface downstream.

• Working as a peer to the deep learning team You will contribute directly to model design and biological framing, not only to data preparation. This means having informed views on feature representation, embedding strategies, and multi-omics data fusion for microbial systems, and being able to evaluate model outputs from a biological standpoint, catching what looks statistically sound but biologically implausible.

• Scientific contribution You will contribute to publications, conferences, and intellectual property arising from this work, and collaborate closely with microbiologists, computer vision experts, and optical physicists to keep the biological interpretation of our models grounded.

About you

We are looking for someone who takes ownership, works with rigour in a fast-moving environment, and finds genuine satisfaction in building a scientific programme that others can rely on. We value curiosity, initiative, and a growth mindset, along with a strong critical awareness of what a given method can and cannot tell you.

• Academic background: A PhD in microbial genomics, computational microbiology, bioinformatics, or a closely related discipline is required.

• Multi-omics expertise: +10y ofdirect experience with genomics, transcriptomics, and at least one of proteomics or metabolomics in a microbial context is required. You should be comfortable moving between data layers and know which one to reach for