We’re partnering with an exciting early-stage biotech in Edinburgh that is building cutting-edge machine learning tools to unlock new understanding in gene regulation, synthetic biology, and next-generation therapeutics.
This is a rare opportunity for an ML Engineer to join a highly ambitious, science-led team at an early stage and help scale a machine learning platform with real-world biological impact.
The Opportunity
You’ll work at the intersection of machine learning engineering, data infrastructure, and applied research, helping translate novel scientific ideas into robust ML systems.
Key areas of focus include:
* Building and optimising deep learning models (PyTorch)
* Developing and maintaining scalable data pipelines for large genomics datasets
* Experiment tracking, benchmarking & reproducibility
* Improving model training efficiency and infrastructure
* Supporting deployment and model lifecycle management
* Contributing to AI/agentic workflows for scientific automation
We’re looking for:
* Strong Python & PyTorch experience
* Experience training / evaluating deep learning models in production or research environments
* A solid grounding in ML engineering best practice (pipelines, versioning, benchmarking, cloud compute)
* Experience working with large-scale datasets
* Bonus points for transformers, biological sequence data, workflow tooling, or LLM / agentic AI exposure
Why join?
* Work on genuinely novel ML applications in biotech
* High ownership and visibility from day one
* Collaborative, research-driven culture
* Dedicated learning & development time
* Edinburgh-based with flexibility
If you’re excited by applied ML, infrastructure, and solving hard scientific problems, I’d love to speak with you.
Apply as directed above, or by sending your CV to cp@kleboejardine.com for a confidential discussion.