Cambridge Centre for Machine Learning Engineer (Fixed Term)
Department of Applied Mathematics and Theoretical Physics
We are looking for an accomplished Machine Learning Engineer to join the newly established Cambridge Centre for AI in Medicine (CCAIM) at the University of Cambridge. CCAIM is a multidisciplinary centre created to produce cutting-edge machine learning (ML) research to solve complex problems in biomedical science, medical discovery and healthcare delivery. Funded by AstraZeneca and GSK - and with strong research ties to the NHS - CCAIM will also drive the revolution in ML-powered precision medicine.
The position is available to start as soon as possible and would be offered as a five-year fixed-term contract in the first instance.
The Machine Learning Engineer will be responsible for designing and building bespoke software packages for the Centre's groundbreaking algorithms, models, and techniques by working closely with researchers to bring recently published methods into a unified and publicly available framework. Projects will be novel, diverse, challenging, and impactful, with examples ranging from designing software to preserve the privacy of patient data to building an end-to-end automated machine learning pipeline to aid clinical decision support.
Given the exploratory nature of the research in question, this role will appeal to anyone who welcomes the chance to solve engineering challenges that have yet to be formulated, let alone attempted. There will be substantial scope for creative development work.
The successful candidate will hold a BSc or MSc in Computer Science or an equivalent discipline, and will have a thorough understanding of mathematics, probability, statistics, algorithms, data structures, software architecture and design. They will be highly proficient in Python, and have considerable experience with autodiff frameworks (Tensorflow, PyTorch, JAX) and data/numeric libraries (numpy, pandas, sklearn, keras). Experience with software at scale and contribution to open source projects will be considered very favourably. Required competencies include strong project management, ability to work constructively with colleagues from a variety of international backgrounds, and a clear aptitude for translating theoretical work into real-world application.