Cambridge Centre for Machine Learning Engineer (Fixed Term)
Department of Computer Science and Technology, West Cambridge
Fixed-term: The funds for this post are available until 31 January 2026.
Artificial intelligence (AI) has the potential to become an engine for scientific discovery across disciplines - from predicting the impact of climate change, to using genetic data to create new healthcare treatments, and from finding new astronomical phenomena to identifying new materials here on Earth.
The Accelerate Programme for Scientific Discovery (website: https://acceleratescience.github.io/index.html) is a high-profile University initiative promoting the use of machine learning to tackle major scientific challenges. Working across disciplines and across the University, Accelerate will:
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provide researchers with specialised training in AI techniques, equipping them with the skills they need to use machine learning and AI to power their research.
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pursue an ambitious research agenda that applies machine learning to the scientific challenges of the 21st century.
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build a community of researchers working at the interface of machine learning and the sciences to share knowledge and experiences that help advance the use of machine learning in the sciences.
Software development is a highly valuable resource that includes modelling, simulation and data-analysis. Generating well-designed software will in turn increase the scope, productivity, reliability, replicability and therefore openness of research. In pursuit of these goals, we are seeking a Machine Learning Engineer (MLE) to help develop our software culture.
The role-holder will contribute to software development activities that facilitate the application of machine learning for scientific discovery, which may include leading specific development projects within Accelerate's portfolio. By advising on the development of research projects and providing support to researchers across the University, the role-holder will contribute to an environment in which researchers from across domains are empowered to build high-quality research software. The role-holder will be responsible for embedding good practice in scientific programming in research supported by Accelerate and for contributing to Accelerate's teaching and learning activities. They will also contribute to the engagement activities supported by the Programme, promoting the importance of software engineering in research and supporting the uptake of best practice. For further information about the likely tasks associated with the role, please consult the further particulars published alongside this advert.
Across the University, there is already a diverse range of research projects using machine learning. The role-holder will have opportunities to pursue collaborations across Departments, working with the diverse community of researchers already associated with Accelerate. Examples of the type of research projects currently associated with the Programme can be found on our website (link: https://acceleratescience.github.io/index.html). The role-holder will also contribute to the development of software tools for the ML and the Physical World lecture course (website: https://mlatcl.github.io/mlphysical/) and other teaching activities associated with the Programme.