Senior Machine Learning Engineer

Senior ML Engineer - Biomedical Foundation Models

London - Hybrid (3 days a week in the office)

We are partnering with a research-driven biotechnology company that is building next generation AI systems that help identify new therapeutic opportunities and accelerate the drug discovery process.

This is a hands-on engineering role for someone who enjoys turning state-of-the-art research into robust, scalable production systems. You'll work closely with AI Scientists from the earliest stages of model development, ensuring that research ideas become reliable, high-performance software used throughout the organisation.

You'll contribute to the design, development and production of large-scale biomedical AI models, bringing engineering expertise into architectural decisions from day one. Working alongside researchers and MLOps engineers, you'll help build production ready machine learning systems that are scalable, maintainable and built to the highest engineering standards.

This is an opportunity to work at the intersection of software engineering, machine learning and biology.

Key Responsibilities

• Partner with AI Scientists to transform validated research into production-ready machine learning systems.
• Contribute to the architecture and implementation of large-scale foundation models, ensuring they are efficient, scalable and deployment-ready.
• Develop high-quality training pipelines, data loaders, tokenisation frameworks, inference services and fine-tuning workflows.
• Build clean, maintainable and thoroughly tested Python code following software engineering best practices.
• Benchmark and evaluate model performance while helping optimise training efficiency and scalability.
• Collaborate closely with MLOps teams to ensure smooth deployment, documentation and ongoing model maintenance.
• Produce comprehensive technical documentation covering model capabilities, limitations and retraining strategies.
• Stay up to date with emerging developments in machine learning engineering, distributed training and biomedical AI.

Requirements;

• A PhD in Machine Learning, Computer Science, Computational Biology or similar. Plus 3-6 years of post study work experience, working with biomedical datasets such as omics, clinical or imaging data.
• Strong experience developing deep learning models and foundation model architectures, including transformers, pre-training and fine-tuning.
• Extensive experience taking machine learning research from prototype through to production-quality deployment.
• Excellent Python programming skills and experience with frameworks eg PyTorch or JAX.
• Strong software engineering fundamentals, including testing, documentation, code reviews and version control.
• Experience with distributed training technologies such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train.
• Experience working alongside research scientists to deliver production machine learning systems.

This is an opportunity to build cutting edge AI systems that have real world impact, and have real influence on the drug discovery process, to be considered.