Location
London

Hours
Full Time

Salary
Competitive, commensurate with experience

About the Role
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. With offices in Cambridge and London, you will join a multidisciplinary team of clinicians, scientists, and engineers united by an open culture, continuous learning mindset, and a shared mission to help biopharma run faster, smarter trials.

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 seeking a Senior Machine Learning Scientist to research, develop, and specialise in state-of-the-art deep learning models for volumetric CT data.

This 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. You will work within the Machine Learning function alongside ML Engineers, Data Scientists, Clinical Scientists, and Imaging Specialists to translate research ideas into reproducible model development workflows and clinically useful outputs.

Key responsibilities include researching and evaluating state-of-the-art computer vision methods for volumetric CT data, contributing to self-supervised pre-training of large foundation models for 3D vision, training and specialising models for supervised tasks, and translating model outputs into biomarkers that support clinical-trial endpoints, enrichment strategies, and disease-characterisation workflows. You will collaborate closely with clinical and scientific stakeholders to ensure model objectives are clinically meaningful, write high-quality Python and PyTorch code, and work with ML Engineers to train large models across GPUs and compute nodes, improving performance and scalability. Proactive communication, clear progress updates, and early risk identification are essential.

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

About you
- Motivated by impactful work in healthcare and clinical research
- Comfortable working in a multidisciplinary environment and translating research into clinical applications
- Able to communicate complex technical concepts clearly to diverse stakeholders

Qualifications
Even better if you have:
- Experience with volumetric CT, medical imaging formats such as DICOM, NIfTI, or NRRD
- Experience training models across multiple GPUs or compute nodes
- Experience with cloud environments
- Experience with typed Python or other typed programming languages
- A publication record in machine learning, computer vision, medical imaging, or related fields
- Experience working in clinical trial, pharmaceutical, biotech, or regulated healthcare environments