Machine Learning Research Software Engineer

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 an experienced Machine Learning Research Software Engineer to lead the development of our software culture.

The Machine Learning Research Software Engineer will lead software development activities that facilitate the application of machine learning for scientific discovery. By providing software engineering support, advising on the development of research projects and delivering training and mentoring to researchers, the role-holder will be responsible for creating an environment that embeds good practice in scientific programming in research. They will be an evangelist for the role of software engineering in machine learning research. In particular, they will:

Guide scientists in software engineering best practices and help to implement these within the teams.

Work closely with scientists to help scope, gather requirements for, and design their applications.

Develop software in tandem with climate science teams, including extending existing code.

Identify and implement opportunities to improve the performance, sustainability, and quality of the applications.

Demonstrate a customer service orientation, giving priority to high customer satisfaction.

Interface with the Institute's research team to help gather problems that then feed into the research programme of the institute.

We welcome applications from individuals who wish to be considered for part-time working or other flexible working arrangements. Conversations about flexible working are encouraged at the University of Cambridge. Please feel free to discuss flexibility prior to applying (using the contact information below) or at interview if your application is successful.

The Institute of Astronomy (IoA) at Cambridge University is internationally renowned for its outstanding environment in data-intensive astronomy. The interdepartmental Kavli Institute for Cosmology, Cambridge (KICC) is co-located on the IoA site, fostering connections with other groups conducting complementary research across Cambridge. Machine learning is increasingly at the heart of leveraging enormous and complex astronomical data sets to solve major scientific challenges in our understanding of the Universe. The IoA, in collaboration with the University Research Computing Service and the University of Cambridge Open Zettascale Lab (COZL) [https://www.zettascale.hpc.cam.ac.uk] is investing in specialist research software engineering support to:

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.

Pursue an ambitious research agenda that applies machine learning to the scientific challenges of the 21st century.

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.