This is a commercial team, and the work is grounded in real operational problems rather than internal roadmaps. The role demands precision, judgement and a deep understanding of how machine learning behaves in production, at scale, in environments that do not forgive downtime. If you are excited by complex problems with life-changing impact and want to build the systems that get technology like this into the most critical environments in healthcare, we would love to hear from you.

Job Responsibilities

• Take models built by the machine learning engineers / scientists and turn them into production services that meet our latency, cost and reliability targets.
• Build and maintain SageMaker training, processing and inference workloads, and the pipelines that orchestrate them.
• Make training runs reproducible and configuration-driven, so any result can be rebuilt from code.
• Build monitoring for model performance, data drift and system health, and make sure the right people are alerted when something moves.
• Ship infrastructure through code review rather than the console, using Terraform and a proper pull-request workflow.
• Document what you build and raise the engineering standard around you.

Requirements

Essential

• Expert Python, with deep hands-on PyTorch experience (TensorFlow also welcome) and the surrounding ecosystem: Hugging Face Transformers, timm, scikit-learn, NumPy, pandas, OpenCV.
• Production experience on AWS, specifically Amazon SageMaker: training and processing jobs, pipelines, model registry, endpoints.
• Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration-driven training runs.
• Containerisation and infrastructure as code: Docker and CUDA-based GPU images, plus enough Terraform to ship your own model to an environment through code review rather than a console.
• Strong software engineering fundamentals: Git and pull-request workflow, automated testing, CI/CD.
• Able to explain technical trade-offs clearly to people who are not engineers.

Desirable

• Event-driven and streaming architectures on AWS: Step Functions, Lambda, Kinesis, ECS, DynamoDB.
• Model monitoring in production: inference logging and drift detection.
• Exposure to healthcare, medical devices or another regulated industry.

Why Work for Proximie?

• You will be encouraged to grow in your role, take ownership and gain responsibilities.
• Generous annual leave.
• Two “well-being” days per year plus the day off for your birthday.
• Summer Fridays” – early office closing on Fridays during summer months.
• Annual bonus programme – based on individual contribution.
• To support your professional growth, all permanent employees will have access to an annual stipend of £/$1,000 to assist with personal development activities.
• Flexible working hours - we trust our people to manage their time and to focus on wider results.
• A flat organizational structure where every opinion matters, ideas are cultivated, and innovation is encouraged.
• Proximie is a truly global company with teams across the UK, Europe, United States, and the Middle East with that you will have opportunities to see the world.

Proximie is an equal opportunity employer. We are committed to providing a work environment that supports, inspires, and respects all individuals. We do not discriminate on the basis of race, colour, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under the law.