Machine Learning Research Engineer (AI/Biotechnology)

TL;DR

Machine Learning Research Engineer (AI/Biotechnology): Integrating generative AI models into a molecular glue discovery platform with an accent on translating research prototypes into production-ready systems and designing scalable infrastructure. Focus on optimizing distributed training, accelerating experimental cycles, and establishing engineering standards for reproducible scientific workflows.

Location: Remote, must be based in the UK or EU

Salary: Competitive salary plus equity

Company

A venture-backed biotechnology company applying advanced machine learning to sustainable agriculture, focused on developing next-generation herbicides.

What you will do

• Translate research prototypes into production-ready machine learning systems, implementing and refining models for reliability and performance.
• Design and maintain infrastructure for data ingestion, preprocessing, training, evaluation, and large-scale inference.
• Optimise distributed training and inference workloads across GPU clusters, cloud platforms, or high-performance computing environments.
• Collaborate with research scientists to accelerate experimental cycles and implement robust experiment-tracking.
• Establish and maintain engineering standards, contributing to code reviews and documentation practices.

Requirements

• A PhD or MSc in Computer Science, Applied Mathematics, Statistics, 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 (AWS or GCP) or HPC environments, and a solid grounding in software engineering best practices.
• 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.
• An interest in applied AI for scientific discovery and motivation to enable researchers through robust engineering systems.

Culture & Benefits

• Competitive compensation and meaningful equity.
• Fully remote structure with regular in-person team gatherings.
• Support for publishing, attending conferences, and contributing to intellectual property development.
• Culture grounded in rigour, intellectual honesty, and shared ownership.
• Engineering excellence directly accelerates scientific progress.