Machine Learning Researcher

KEMIO Consulting; Greater London; Hybrid; GBP 75,000 - 120,000; Full time

Job summary

KEMIO Consulting partners with a biotech company in London to recruit a Senior Machine Learning Engineer focused on turning research into production-ready AI systems. The role is hybrid, with three days in the office weekly, and involves delivering scalable, high-performance biomedical ML solutions.

You will work with AI Scientists and MLOps to design, build and deploy training pipelines, inference services and robust architectures, ensuring reliability and maintainability across production

Qualifications

• PhD in Machine Learning, Computer Science, Computational Biology or similar; 3–6 years post-study experience, biomedical datasets (omics/clinical/imaging).
• Strong experience developing deep learning models and foundation model architectures including transformers, pre-training and fine-tuning.
• Extensive experience taking ML research from prototype to production-ready deployment.
• Excellent Python programming skills and experience with frameworks 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 with researchers to deliver production ML systems.

Responsibilities

• Transform validated research into production-ready machine learning systems.
• Contribute to architecture and implementation of large-scale foundation models for efficiency and deployment-readiness.
• Develop training pipelines, data loaders, tokenisation frameworks, inference services and fine-tuning workflows.
• Build clean, maintainable, well-tested Python code following engineering best practices.
• Benchmark model performance and optimise training efficiency and scalability.
• Collaborate with MLOps teams to ensure smooth deployment and model maintenance.
• Produce comprehensive technical documentation on model capabilities, limitations and retraining strategies.
• Stay up to date with developments in ML engineering and biomedical AI.

Skills

Python

PyTorch

JAX

Transformers

Deep Learning

Software Engineering

Testing

Education

PhD in ML/CS/Computational Biology or similar

Tools

PyTorch Distributed

DeepSpeed

FSDP

Ray Train

Python tooling

Job description

Senior Machine Learning Engineer - Productionising models

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

We are partnering with an innovative, 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.

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.