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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Bio Machine Learning Engineer - **Company:** SR2 - **Location:** Oxford, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Bioinformatics, Computational Biology, Python (Programming Language), Machine Learning, Tensorflow, Pytorch, Large Language Models, Deep Learning, Model Validation, Machine Learning Operations, Data Pipelines - **Published:** August 26, 2026 - **Apply:** https://www.collegerecruiter.com/job/2815096441-lead-bio-machine-learning-engineer ## About the Role * 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 ## Description 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 (). ## Related Videos - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [30 Golden Rules of Deep Learning Performance](https://www.wearedevelopers.com/videos/11-30-golden-rules-of-deep-learning-performance) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)