At a Glance
- Tasks: Develop and optimise machine learning models for 3D medical imaging.
- Company: Join Qureight, a pioneering company transforming clinical trials with AI.
- Benefits: Enjoy competitive pay, private medical insurance, and generous annual leave.
- Other info: Diverse team culture with opportunities for continuous learning and growth.
- Why this job: Make a real impact in healthcare by accelerating breakthroughs for patients.
- Qualifications: Strong Python and PyTorch skills; experience with ML pipelines and cloud environments.
The predicted salary is between 63000 - 77000 £ per year.
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 engineering capability required to train, optimise and deploy large-scale 3D medical-imaging models reliably.
We are looking for a Machine Learning Engineer to focus on the development, training, optimisation and inference of state-of-the-art computer vision models applied to volumetric CT data.
The role focuses on ensuring that models can be trained efficiently, deployed securely, and operate at scale.
This role sits within the Machine Learning function and works closely with ML Scientists, Dev Ops, Data Engineering and Software Engineering teams to turn research models into robust, scalable and reproducible training and inference workflows.
What you will do
- Develop robust, scalable and reproducible inference pipelines.
- Deploy models into production using ONNX, Tensor RT or similar frameworks.
- Build and optimise scalable machine learning training workflows.
- Optimise data loading, logging, checkpointing and resource utilisation for large-scale model training.
- Act as a bridge between research and production, translating research into reliable, maintainable and scalable engineering components.
- Support cloud-based ML infrastructure such as MLFlow.
- Create and maintain CI pipelines for model training, testing and deployment workflows.
- Collaborate with Dev Ops and infrastructure teams on deployment patterns and infrastructure as code.
- Improve reproducibility, traceability and quality across ML engineering workflows.
- Identify risks, communicate trade-offs and proactively improve tooling and processes.
- Strong Python and Py Torch skills, with the ability to work confidently in model training and inference codebases.
- Experience building, optimising and maintaining ML training and inference pipelines.
- Experience with Docker and containerised ML workflows.
- Experience with model deployment and optimisation frameworks such as ONNX, Tensor RT or similar tools.
- Experience with GPU-based training, model serving and compute optimisation.
- Experience building and maintaining CI pipelines.
- Experience with cloud environments.
- Strong knowledge of Linux shells, git and modern Python development tools such as uv, poetry, ruff, black, mypy or ty.
- Experience with modern development workflows, including pull requests, code review, documentation and ticketing.
- Strong communication skills and ability to work across ML Science, Dev Ops, Data Engineering and Software Engineering teams.
- Even better ifyou have
- Experience working with 3D medical imaging, CT data, DICOM, NIf TI or NRRD formats.
- Experience deploying models in regulated, clinical, pharmaceutical or healthcare environments.
- Experience with infrastructure as code, such as Terraform.
- Experience with multi-stage Docker containers and secure software supply-chain practices.
- Experience with distributed training, large-scale data loading or high-performance computing environments.
- Experience supporting research-to-production ML workflows.
- Experience with monitoring, observability, model versioning or MLflow-like tooling.
- A comprehensive benefits package that includesan annual bonus plan, private medical insurance, life insurance, and acontributory pension scheme
- 25 days annual leave, plus bank holidays and enhanced maternity leave
- A diverse work environment that brings together experts in many fields, including software engineering, Dev Ops, data science, machine learning, quality assurance, regulatory affairs, and clinical operations.
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.
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Senior Machine Learning Engineer employer: Qureight Ltd
Qureight Ltd is an exceptional employer, offering a dynamic work environment where innovation meets collaboration. As a fast-growing data-driven biotech company based in the UK, employees benefit from a culture that prioritises professional development and meaningful contributions to groundbreaking projects. With opportunities for exposure to high-stakes transactions and a supportive team, this role promises not just a job, but a rewarding career path in a thriving industry.
