We recruited for a Research Engineer (Machine Learning) to support the development of a generative modelling and scientific discovery platform at a technology company operating at the intersection of AI and scientific research. The role focused on applying machine learning and software engineering expertise to translate research models into scalable, reliable production systems and infrastructure.

The position involved integrating generative AI and machine learning models into the company’s discovery platform, building and maintaining machine learning infrastructure for data processing, training, inference, and evaluation, and optimising distributed training pipelines using GPUs and cloud or cluster computing environments. The role also included implementing monitoring, experiment tracking, and reproducibility best practices, and collaborating closely with scientists and engineers to accelerate experimentation and research productivity.

The role required a strong background in machine learning engineering and software development, experience building ML infrastructure and working with large-scale training and deployment workflows, and strong proficiency with PyTorch, cloud platforms, containerisation, and CI/CD tooling. Experience working in research-driven environments and collaborating with scientific teams was also important.