Machine Learning Research Engineer

Remote (UK/EU based) / Full-time / Competitive salary plus equity commensurate with experience

About the Company

A venture-backed biotechnology company applying advanced machine learning to sustainable agriculture is building a computational platform for molecular glue discovery. With support from leading European investors, the company is focused on translating targeted protein degradation into practical agricultural solutions.

Its initial objective is to develop next-generation herbicides that improve crop protection while reducing environmental impact. By combining deep expertise in machine learning, computational chemistry, and biology, the organisation is establishing a rigorous, data-driven approach to discovery in an area of significant global importance.

The Role

The company is seeking a Research Engineer in Machine Learning to integrate generative AI models into its molecular glue discovery and design platform. This role sits at the intersection of research and production engineering, working closely with machine learning scientists to transform promising research ideas into reliable, scalable systems.

The successful candidate will take state-of-the-art models from academic literature and experimental repositories and develop them into well-structured, maintainable codebases that can operate across the broader discovery pipeline. They will design and manage infrastructure that supports rapid experimentation while maintaining high standards of reproducibility, robustness, and performance.

This is a hands-on engineering position requiring both depth in modern machine learning workflows and strong software engineering fundamentals. The Research Engineer will play a central role in ensuring that research models are not only innovative, but usable and dependable in real-world scientific workflows.

What You’ll Do

The appointee will translate research prototypes into production-ready machine learning systems, implementing, testing, and refining models for reliability and performance. They will design and maintain infrastructure for data ingestion, preprocessing, training, evaluation, and large-scale inference, ensuring smooth integration across computational and scientific teams.

They will optimise distributed training and inference workloads across GPU clusters, cloud platforms, or high-performance computing environments, improving efficiency and scalability. In collaboration with research scientists, they will accelerate experimental cycles, validate findings, and implement robust experiment-tracking and monitoring frameworks to ensure reproducibility.

The role also involves establishing and maintaining engineering standards, contributing to code reviews, improving documentation practices, and embedding testing and continuous integration processes. The Research Engineer will help cultivate a technically rigorous environment in which scientific ambition is supported by dependable, well-architected systems.

What We’re Looking For

Required

• A PhD or MSc in Computer Science, Applied Mathematics, Statistics, or a related technical discipline, or equivalent industry experience in research-intensive environments.
• At least two years of experience in fast-paced research or engineering settings, ideally within early-stage or high-growth technology organisations.
• Demonstrated expertise in building and managing machine learning infrastructure for large-scale training, inference, and deployment.
• Strong proficiency in PyTorch and modern MLOps or DevOps tooling, including experiment tracking, containerisation, orchestration, and CI/CD workflows.
• Experience working with cloud platforms (such as AWS or GCP) or HPC environments, and a solid grounding in software engineering best practices including testing, version control, monitoring, and documentation.
• Excellent communication skills and a clear commitment to reproducible, collaborative research engineering.

Nice to Have

• Experience designing or extending distributed training and optimisation pipelines at scale.
• Familiarity with experiment-tracking platforms and infrastructure-as-code tooling.
• Exposure to bioinformatics, cheminformatics, or molecular simulation toolkits and their integration with machine learning workflows.
• An interest in applied AI for scientific discovery and a strong motivation to enable researchers through robust engineering systems.

Why Join?

This is an opportunity to apply advanced machine learning to a scientifically demanding and societally significant domain. The company offers competitive compensation and meaningful equity, alongside a fully remote structure with regular in-person team gatherings.

Team members are supported in publishing, attending conferences, and contributing to intellectual property development. The organisation fosters a culture grounded in rigour, intellectual honesty, and shared ownership, where engineering excellence directly accelerates scientific progress.