PE Collective
MLOps engineers keep ML systems running in production. They build the infrastructure for model training, deployment, monitoring, and retraining. As companies move from ML experiments to production workloads, demand for MLOps has grown steadily. The role sits at the intersection of DevOps, data engineering, and machine learning, and it pays accordingly.
Key Skills That Drive Higher Pay
Top Paying Companies
Frequently Asked Questions
What is the salary range for MLOps engineers?
MLOps engineer salaries range from $85K at entry level to $270K for senior roles at top companies. The median is around $190K. Companies with large-scale ML systems (autonomous vehicles, ad tech, financial services) tend to pay at the higher end.
How is MLOps different from regular DevOps?
MLOps adds model-specific concerns on top of standard DevOps: data versioning, model performance monitoring, GPU cluster management, feature stores, and A/B testing infrastructure. The tooling is specialized (MLflow, Kubeflow, Weights & Biases), and you need enough ML knowledge to debug model-related issues in production.
What background leads to MLOps roles?
Most MLOps engineers come from either DevOps/SRE backgrounds (adding ML knowledge) or ML engineering backgrounds (adding infrastructure skills). Both paths work. Strong Kubernetes and Python skills are table stakes.
Methodology
Salary data is collected from job postings on Indeed and company career pages. Only jobs with disclosed compensation are included. Data is updated weekly.
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