Senior Machine Learning Scientist

About us

Qureight’s mission is to accelerate clinical trials and ensure breakthroughs in lung and heart disease reach patients without delay. Our AI-powered data and imaging curation platform enables the analysis of clinical imaging and other healthcare data, helping our customers bring treatments to market, faster.

We’re looking for talented people who want their work to matter. With offices in Cambridge and London, you’ll join our multidisciplinary team of clinicians, scientists, and engineers. What unites us is our open culture, continuous learning mindset, and a shared mission to help biopharma run faster, smarter trials.

About the role

As Qureight scales its AI-driven imaging platform and expands its work with pharmaceutical and clinical partners, we are building the machine learning research capability required to develop robust, clinically meaningful models for 3D medical imaging.

We are looking for a Senior Machine Learning Scientist to research, develop and specialise state-of-the-art deep learning models for volumetric CT data. The role focuses on foundation-model pre-training, supervised model specialisation, segmentation, classification, regression and biomarker extraction pipelines that support clinical-trial and real-world evidence programmes.

This role sits within the Machine Learning function and works closely with ML Engineers, Data Scientists, Clinical Scientists and Imaging Specialists to ensure that research ideas are translated into reproducible model development workflows and clinically useful outputs.

What you will do

• Research, develop, and evaluate state-of-the-art computer vision methods for volumetric CT data.
• Contribute to self-supervised pre-training of large foundation models for 3D vision.
• Train and specialise models for supervised tasks, including segmentation, classification and regression.
• Keep up to date with the latest machine learning research and contribute to internal reading groups.
• Contribute to the publication of high-impact conference, journal and sponsor-facing scientific outputs.
• Write high-quality Python and PyTorch code for model training, inference and evaluation.
• Translate model outputs into biomarkers that support clinical-trial endpoints, enrichment strategies and disease-characterisation workflows.
• Work with clinical and scientific stakeholders to ensure model objectives are clinically meaningful.
• Train large models across GPUs and compute nodes, working with ML Engineers to improve performance and scalability.
• Work proactively, communicate progress clearly and identify risks early.

Requirements

• Strong experience in machine learning research, with experience in either computer vision, foundation models, or 3D medical imaging.
• Deep understanding of machine learning theory and its background, including deep learning, linear algebra, probability theory, statistics and optimisation.
• Strong Python and PyTorch skills, with the ability to work confidently in model training and inference codebases.
• Experience developing and evaluating deep learning models for imaging, segmentation, classification, regression or representation learning.
• Experience with self-supervised learning, foundation models or large-scale model training.
• Experience writing production-quality or research-to-production Python code.
• Experience with modern software development workflows, including git, pull requests, code review, documentation and ticketing.
• Strong communication skills and ability to work across ML, data science, clinical science and engineering teams.
• Ability to be proactive, self-sufficient and rigorous when completing tasks.
• Strong knowledge of Linux shells, git and modern Python development tools such as uv, poetry, ruff, black, mypy or ty.

Even better if you have

• Experience with volumetric CT, medical imaging, DICOM, NIfTI or NRRD data.
• Experience training models across multiple GPUs or compute nodes.
• Experience with cloud environments.
• Experience with typed Python or typed programming languages.
• Publication record in machine learning, computer vision, medical imaging or related fields.
• Experience working in clinical trial, pharmaceutical, biotech or regulated healthcare environments.

Benefits

What can you expect from us

• A comprehensive benefits package that includes an annual bonus plan, private medical insurance, life insurance, cycle-to-work scheme, and a contributory pension scheme
• Equity, so that our team can share in the long-term success of Qureight
• 28 days’ holiday allowance, 25 days’ annual leave and 3 additional company days between Christmas and New Year, plus bank holidays and enhanced family leave
• A diverse work environment that brings together experts in many fields, including software engineering, DevOps, data science, machine learning, quality assurance, regulatory affairs, and clinical operations.

How to apply

Please upload a CV and covering letter by clicking 'Apply Now'. Your covering letter should explain why you are applying for the job and what skills and experience you can bring to the role.

We review CVs as we receive them and interview as soon as we have applications that look like a good match (usually within one week). We do not use closing dates. So, please apply as soon as possible to avoid missing out on this role. We advertised this role on 30th July 2026.

If you have any queries, please contact [email protected]

Everyone is welcome at Qureight. We are an equal opportunities employer and encourage applications from all suitably qualified candidates regardless of age, disability, ethnicity, sex, gender reassignment, religion or belief, sexual orientation, marriage and civil partnership, or pregnancy and maternity.

Women and other underrepresented groups may be less likely to apply for a role unless they meet all or nearly all of the requirements. If this applies to you, we still encourage you to apply – you may be a great fit, even if you don’t meet every qualification. We’d love to hear from you.

If you require any adjustments to the application or selection process, please let us know. We will be happy to support you.