Machine Learning Engineer, Accelerate Programme for Scientific Discovery (Fixed Term)

Fixed-term: The funds for this post are available for 12 months.

Artificial intelligence (AI) has the potential to become an engine for scientific discovery across disciplines. The Accelerate Programme for Scientific Discovery (https://science.ai.cam.ac.uk/) is a high-profile University initiative promoting the use of machine learning to tackle major scientific challenges.

Accelerate Science

• Provides researchers with specialised training in AI techniques, equipping them with the skills they need to use machine learning and AI to power their research.
• Pursues an ambitious research agenda that applies machine learning to the scientific challenges of the 21st century.
• Convenes 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.

Generating well-designed software will increase the scope, productivity, reliability, replicability and openness of research. In pursuit of these goals, we are seeking experienced Machine Learning Engineers (MLE) to lead the development of our software culture.

The role holder will contribute to software development activities that facilitate the application of machine learning for scientific discovery. By advising on the development of research projects and providing support to researchers across the University, role-holders 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. The role holder will provide software support to Accelerate's AI Clinic, which supports Cambridge University researchers to resolve engineering issues they might encounter when implementing machine learning methods (https://science.ai.cam.ac.uk/ai-clinic/). They will contribute to Accelerate's community engagement activities, promoting the importance of software engineering in research and supporting the uptake of best practice.  The role-holder will also contribute to teaching activities within the team, including our training courses (https://science.ai.cam.ac.uk/resources), study groups and lecture courses such as Machine Learning and the Physical World and Advanced Data Science (https://mlatcl.github.io/resources/).

We're looking for experienced machine learning engineers who have

• Deployed in ML-oriented software projects (e.g. the use of agile development techniques to respond to emerging research needs or experience in designing, testing and maintaining scalable data pipelines)
• Sufficient breadth and depth of specialist knowledge in ML software packages to support ML-oriented software projects (e.g. expertise in helping scientists who are not machine learning experts debug and resolve errors encountered when using popular machine learning packages such as Scikit-learn, Hugging Face Transformers, and PyTorch)
• Knowledge and experience of Large Language Models and their applications.
• The ability to communicate technical concepts effectively and enthusiastically across disciplines and to audiences with a range of technical abilities.
• Experience of working in interdisciplinary teams.
• Experience of coaching and mentoring research staff.

The post is offered on a full time basis, however we would be open to discussing part time or flexible working arrangements.

To apply online for this vacancy and to view further information , please click the 'Apply' button above. 

To apply, please submit

• CV and publications list
• Cover letter
• Short summary (max. 1 p.) setting out your view on "what role do you believe software engineering can play in building the Cambridge AI for science community?"
• 2 referees who may be contacted during the application process.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

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