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# Senior Machine Learning Engineer

- **Company:** The Roleas
- **Location:** Cambridge, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Big Data, Code Review, Continuous Integration, Information Engineering, Extract Transform Load (ETL), DevOps, Dicom, Distributed Computing Environment, Python (Programming Language), Machine Learning, Software Engineering, Data Logging, High Performance Computing, Pytorch, Git, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Software Version Control, Docker
- **Published:** September 21, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/senior-machine-learning-engineer/45491042

## About the Role

and deployment workflows.Collaborate with DevOps 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 PyTorch 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, TensorRT 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, DevOps, Data Engineering and Software Engineering teams.Even better ifyou haveExperience working with 3D medical imaging, CT data, DICOM, NIfTI 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 scheme25 days annual leave,plus bank holidays and enhanced maternity leaveA 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.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. #J-18808-Ljbffr

## Description

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, DevOps, Data Engineering and Software Engineering teams to turn research models into robust, scalable and reproducible training and inference workflows.What you will doDevelop robust, scalable and reproducible inference pipelines.Deploy models into production using ONNX, TensorRT 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


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