MLOps Engineers build and maintain the infrastructure for deploying, monitoring, and scaling machine learning models in production. They bridge the gap between data science and DevOps, automating ML pipelines, managing model registries, and ensuring reliable AI systems at scale.
We're currently tracking 50 open MLOps Engineer positions across our listings. These roles span on-site, hybrid, and remote teams at companies of every size, so whether you're looking for your first MLOps Engineer role or a senior leadership seat, there are live openings to explore and apply to today.
Salary range from 20 live MLOps Engineer listings on this page with disclosed yearly pay (USD).
MLOps salaries typically range from $140K to $210K in the US and €75K to €110K in Europe. GPU infrastructure experience and production-scale model serving push offers toward the top of the band. Based on 20 live listings on this page with disclosed yearly pay, the median currently sits around $223K.
Strong Python, model serving and packaging (vLLM, TorchServe, ONNX), MLflow or Kubeflow for pipeline and registry management, data-pipeline tooling like Airflow, Kubernetes, and increasingly GPU scheduling and cost management.
Generally yes — models are trained and served in cloud GPU clusters, not on local hardware. Remote and hybrid MLOps roles dominate this page.
MLOps inherits DevOps practices but adds the data and model lifecycle: versioning datasets, retraining triggers, drift detection, and model registries. The infrastructure skills transfer; the failure modes you manage are new.
Click 'View job' on any card to go straight to the employer's application page. New MLOps positions are added daily from verified company career pages and ATS feeds.
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