Senior Machine Learning Research Engineer

Generative; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Generative is seeking a Senior ML Research Engineer to develop novel ML models and the research infrastructure behind them, spanning metagenomics, metabolomics, 16S and other multi-omic datasets.

Own problems end-to-end—from hypothesis and dataset design to model development, rigorous evaluation and publication-quality research. You’ll collaborate with biological scientists and run GPU/cloud training to scale experiments.

Qualifications

• PhD, publications, or equivalent evidence of independent research is valuable.
• Direct biology experience is not essential; omics or molecular data are a strong plus.
• Experience building and shipping research-driven ML systems.

Responsibilities

• Own problems end-to-end—from hypothesis and dataset design to model development and publication-quality research.
• Design datasets and develop models with rigorous evaluation.
• Build reproducible training and inference infrastructure and scale with GPU/cloud resources.
• Collaborate with biological scientists and researchers on experiments and data.
• Lead towards publications and scientific breakthroughs.

Skills

ML research

Research infrastructure

End-to-end ML

Experimental design

Representation learning

Education

PhD or equivalent

Tools

GPUs

Cloud platforms

Experiment tracking

Job description

Generative is thrilled to be partnering with an AI-native TechBio company building a new generation of machine learning systems for understanding complex biological systems.

Biological data is noisy, high-dimensional and heavily shaped by the experimental process. The challenge is to build models that can separate genuine biological signal from artefacts — and turn large-scale experimental datasets into reliable scientific insight.

They're looking for a Senior ML Research Engineer to develop novel ML models and the research infrastructure behind them, working across metagenomics, metabolomics, 16S and other multi-omic datasets.

This is a highly hands-on research role where you'll own problems end-to-end — from hypothesis and dataset design through model development, rigorous evaluation and publication-quality research.

You'll work across:

• Novel ML models for complex biological data
• Foundation models, pretraining and fine-tuning
• Representation learning across sequences, graphs and molecular data
• Rigorous evaluation of noisy real-world datasets
• Reproducible training and inference infrastructure
• Experimental design and collaboration with biological scientists
• GPU / cloud training and scalable ML systems
• Research leading towards publications and scientific breakthroughs

They're particularly interested in strong ML researchers and engineers with experience building and shipping research-driven ML systems. A PhD, publications or equivalent evidence of independent research is valuable.

Direct biology experience is not essential. Experience with omics, sequencing, molecular data, generative models or scientific/healthcare datasets is a strong plus — but exceptional ML researchers from other domains where data is noisy, high-dimensional or difficult to model are also encouraged to apply.

The company is building a genuine ML Lab-in-the-Loop, where models inform experiments and experimental results continuously feed back into the models. The goal is to create a rapid cycle between data → models → experiments → learning.

If you're an ML researcher who wants to work on fundamental problems in biological intelligence rather than incremental model optimisation, I'd be very interested in speaking.