C
Circadiahealthvia Lever
ML Ops Engineer
Vauxhall, LondonPosted 2mo ago
MLOpsMid LevelFull-time
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About the Role
Position Overview
As an ML Ops Engineer at Circadia Health, you will own the infrastructure and operational lifecycle of the machine learning systems that power our clinical monitoring platform. You will build and maintain the production ML pipelines, deployment infrastructure, and monitoring systems that enable Circadia's predictive models to identify early signs of clinical deterioration.
Reporting to the Principal ML Engineer, you will work across ML, backend, data, and clinical teams to ensure models are reliably trained, versioned, deployed, and monitored in both cloud and edge environments. You will be a key driver in elevating Circadia's ML practice – from reproducibility and experiment tracking to CI/CD for models and operational observability.
This is a high-ownership role at a lean company where production reliability, rapid iteration, and pragmatic engineering are essential. Your work will directly impact patient outcomes by ensuring our predictive models are always running, always accurate, and always improving.
As an ML Ops Engineer at Circadia Health, you will own the infrastructure and operational lifecycle of the machine learning systems that power our clinical monitoring platform. You will build and maintain the production ML pipelines, deployment infrastructure, and monitoring systems that enable Circadia's predictive models to identify early signs of clinical deterioration.
Reporting to the Principal ML Engineer, you will work across ML, backend, data, and clinical teams to ensure models are reliably trained, versioned, deployed, and monitored in both cloud and edge environments. You will be a key driver in elevating Circadia's ML practice – from reproducibility and experiment tracking to CI/CD for models and operational observability.
This is a high-ownership role at a lean company where production reliability, rapid iteration, and pragmatic engineering are essential. Your work will directly impact patient outcomes by ensuring our predictive models are always running, always accurate, and always improving.
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