Senior Machine Learning Operations Engineer

Who you are

• 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
• 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
• 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

What the job involves

• As Proximie continues to turn every activity and event in the operating room into comprehensive, structured, and context-rich data that drives better insights and decision making, you will be the engineer who makes that intelligence dependable at scale
• Using your deep technical expertise and real-world production experience, you will own the platform and the infrastructure that carry our machine learning solutions out of development and into hundreds of live hospitals, and keep them performing once they are there
• Our machine learning engineers own the models. You own everything that gets those models in front of a customer and keeps them running: the training and deployment infrastructure on AWS and SageMaker, reproducible and configuration-driven training runs, environments defined in code, release pipelines that make a deployment routine rather than an event, and the monitoring that tells us when a model's behaviour shifts in a theatre on the other side of the world
• You will make the difference between a solution that works and a solution a hospital can depend on
• 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
• 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

Benefits

• Bi-annual bonus programme – based on individual contribution and company performance
• 25 days of paid leave
• Full private medical cover (optional)
• Central London location near Oxford Circus
• Career advancement opportunities
• Well being days annually and volunteer days
• Summer Fridays