Research Engineer, Machine Learning

A leading technology company operating at the intersection of AI and scientific research was looking for a Research Engineer (Machine Learning) who would play a central role in advancing its generative modelling and discovery platform. This role offered the opportunity to apply machine learning and software engineering expertise to help transform cutting-edge research into scalable, reliable systems.

Our client was developing an AI-driven platform designed to support complex scientific discovery with a focus on sustainability and real-world impact. The team combined expertise across machine learning, biology, chemistry, and engineering, working collaboratively to build tools that enable faster experimentation and high-quality insights.

Key responsibilities

• Work closely with research and engineering teams to integrate generative AI and machine learning models into the company’s discovery platform.
• Translate research prototypes into well-structured, maintainable code suitable for production-level workflows.
• Design and maintain infrastructure to support data ingestion, preprocessing, training, inference, and evaluation at scale.
• Optimise distributed training and inference pipelines, including the use of GPUs and cloud or cluster computing environments.
• Implement monitoring, logging, experiment tracking, and reproducibility best practices across ML workflows.
• Partner with scientists and domain experts to accelerate experimentation cycles and improve research productivity.
• Contribute to engineering standards through documentation, code reviews, and shared best practices.

Required experience and skills

• MSc or PhD in Computer Science, Mathematics, Statistics, or a related technical field (or equivalent research/industry experience).
• Experience working in fast-paced research or engineering environments, ideally within smaller or early-stage teams.
• Demonstrated ability to build and maintain machine learning infrastructure for large-scale training, inference, and deployment.
• Experience working with complex research codebases and contributing to or extending open-source frameworks.
• Strong proficiency with PyTorch and wider ML engineering tooling, including Docker, Kubernetes, CI/CD systems, and cloud platforms.
• Solid software engineering fundamentals, including testing, reproducibility, version control, and documentation.
• Excellent communication skills and a proactive, delivery-focused working style.