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Help us bridge the gap between diagnosis and therapy by integrating genomics, proteomics, metabolomics, and more to subtype autism
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ML Engineer
Gutz Technologies is an AI-native company building models of how human biology changes over time — using multi-omics data from 17,000 individuals as part of Wellcome LEAP's $50M FORM program. We're looking for an ML Engineer to design novel machine learning architectures custom-built for biology, working on top of our proprietary probabilistic programming language for Bayesian neural networks. You'll develop new model architectures that capture causal relationships across biological systems, scale training across tens of thousands of subjects and multiple data modalities, and build inference infrastructure that lets collaborating research teams run predictions on their own cohorts. This role sits at the intersection of our engineering and science teams — you'll partner closely with bioinformaticians and domain scientists to turn complex biological data into actionable, causal models. If you want to push the boundaries of what ML can do in biology — not just apply off-the-shelf methods, but invent new ones — we'd love to hear from you.
Apply Now →Bioinformatician
Gutz Technologies is an AI-native company analyzing multi-omics data from 17,000 individuals as part of Wellcome LEAP's $50M FORM program, investigating the link between the gut microbiome and neurodevelopment. We're looking for a bioinformatician to help build large AI models custom-built for biology from first principles. Working alongside our data infrastructure team, you'll be involved in data QC, constructing biological priors that encode domain knowledge into our models, fitting models across multiple omics layers, and inspecting and debugging terabytes of model outputs to ensure biological plausibility. This isn't a traditional pipeline role — your expertise in biology is what makes the AI work. You'll bridge the gap between raw biological data and causal models of disease, ensuring that what our models learn actually makes sense. Experience with multi-cohort data integration, comfort interpreting large-scale computational outputs, and strong biological intuition are all key. If you want to do computational biology where your domain expertise directly shapes the AI — not just feeds it — this is the role.
Apply Now →Interested in Joining Us?
Send your CV and a brief note about yourself to hello@gutztechnologies.com
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