Senior Machine Learning Engineer (Platform)

Build and operate ML pipelines - Own the training, evaluation, and deployment pipelines end-to-end, from data ingestion through to models running in customer environments.

Automate the path to production - Build CI/CD for models, artefact and model registries, reproducible environments, and infrastructure as code that takes an experiment to a deployed service without manual steps.

Keep production models healthy - Implement monitoring, alerting, and drift and data quality checks, and act as a first responder when something breaks.

Run distributed training infrastructure - Manage GPU clusters and scheduling, keep jobs efficient and utilisation high, and troubleshoot failures across cloud, on-prem, and neocloud environments.

Ship reliable serving infrastructure - Deploy and scale inference services that handle spiky load across regions and customers, with attention to latency, cost, and data residency requirements.

Remove friction for the ML team - Improve the tooling and workflows ML engineers use daily, and document the standards that keep things consistent as the team grows.

Solid hands-on experience building and operating ML training pipelines, model serving, and monitoring systems in production.

Strong Python, and working knowledge of PyTorch (or equivalent) - enough to debug a training job, not necessarily to design the model.

Practical experience with distributed training and GPU infrastructure, including scheduling, resource management, and diagnosing throughput or memory issues.

Experience in the cloud (AWS, GCP, or Azure), container orchestration (Kubernetes, Docker), and infrastructure as code.

Experience with production model monitoring, data quality frameworks, and preparing training data for ML readiness.

Sound engineering judgement - you write maintainable code, think about failure modes, and know when a quick fix is fine and when it isn't.