I’m working with a well-funded early-stage BioAI startup applying machine learning to a hard, underexplored biological problem with real implications for climate resilience and food security.
They’re building an ML-first platform where research doesn’t sit in isolation. Models are expected to ship, be used by scientists, and influence real experimental decisions.
They’re looking for an ML Engineer who:
* Has a strong grounding in modern ML (generative models, representation learning, geometric deep learning, etc.)
* Has expertise in building and managing ML infrastructure for large-scale training, inference, and deployment
* Has worked with biological or molecular data in some form… proteins, molecules, structure, interfaces
* Can work closely with chemists and biologists, and actually make the models useful
* Has experience delivering in a fast-paced environment
You don’t need prior experience in the exact problem space. Almost nobody has. Adjacent domains and an engineering mindset matter more than perfect overlap.
Fully remote (UK), with 1/4ly London meetups, long runway, real scientific ownership.
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