Lead Bio Machine Learning Engineer

I’m working with an early-stage biotech startup based in Oxford building machine learning-driven platforms for drug discovery. Their mission is to dramatically reduce the time and cost of bringing new therapies to market by combining computational biology, large-scale biological datasets, and modern ML techniques.

Backed by top-tier investors, they’re a small, high-calibre team of scientists and engineers.

Overview

We’re hiring a Lead Bio-ML Engineer to take ownership of our core machine learning systems.

This is a high-impact, high-autonomy role where you’ll:

• Own the end-to-end ML lifecycle (data, modelling, deployment)
• Work closely with biologists to translate experimental problems into ML solutions
• Build and scale models across areas like sequence modelling, structure prediction, and multimodal biological data
• Shape the technical direction and ML roadmap
• Mentor and grow a small team as they scale

What You’ll Do

• Designing models for genomics, proteomics, and drug discovery pipelines
• Applying deep learning / foundation models to biological datasets
• Building robust, production-ready ML systems (not just research prototypes)
• Developing internal tooling for data pipelines, experiment tracking, and model evaluation

What They Want

Core experience:

• Strong background in machine learning / AI engineering
• Experience applying ML to life sciences, bioinformatics, or computational biology
• Production experience with Python + PyTorch / TensorFlow
• Track record of owning and shipping ML systems end-to-end

Nice to have:

• Experience with biological data (e.g. sequencing, protein structures)
• Familiarity with drug discovery or wet-lab collaboration
• Exposure to LLMs / foundation models in biology
• Previous startup or high-ownership environment

Recap

• High ownership: you’ll define and build critical systems from the ground up
• Real-world impact: your work directly contributes to therapeutic discovery
• Tight feedback loop: collaborate daily with domain experts
• Equity upside: meaningful stake in an early-stage company
• Small, elite team: no bureaucracy, just execution

Apply

Please apply here or reach out to me directly on LinkedIn/ via email ().

• Strong background in machine learning / AI engineering
• Experience applying ML to life sciences, bioinformatics, or computational biology
• Production experience with Python + PyTorch / TensorFlow
• Track record of owning and shipping ML systems end-to-end

Nice to have:

• Experience with biological data (e.g. sequencing, protein structures)
• Familiarity with drug discovery or wet-lab collaboration
• Exposure to LLMs / foundation models in biology
• Previous startup or high-ownership environment