MLOps engineers earn an average of $161,246 on Glassdoor, with senior MLOps roles at $205,958 and staff/principal positions reaching $210,000-$257,000. At top-tier companies, compensation is higher: Anyscale pays $270K, Google $260K, Cruise $245K, Two Sigma $240K, and Databricks $220K. MLOps earns 15-25% more than traditional DevOps due to specialized ML knowledge, and ML/MLOps compensation jumped roughly 20% year-over-year through 2025.
Salary by experience level
Entry-level MLOps engineers (0-2 years) earn $85,000-$132,000 depending on the source and company tier. Mid-level (3-5 years) earns $115,000-$175,000. Senior MLOps engineers (5-8 years) command $150,000-$210,000, with Glassdoor reporting $205,958 average for senior roles. Staff and principal positions (8+ years) reach $195,000-$257,000+, with top-tier companies going higher.
The massive salary discrepancy across sources exists because 'MLOps Engineer' means three different sub-roles: ML platform engineer, ML infrastructure engineer, and applied MLOps engineer. ZipRecruiter reports $87K average (capturing a broader, more junior sample), while PE Collective reports $220K average (sampling top-tier companies only). Know which tier you're comparing to.
MLOps vs ML Engineer vs DevOps vs Data Engineer
MLOps engineers earn $130K-$165K national average, with senior range of $168K-$257K. ML/AI Engineers earn $146K-$191K average, with senior reaching $190K-$280K. DevOps Engineers earn $115K-$155K. Data Engineers earn $110K-$150K. MLOps earns slightly less in base than pure ML Engineers, but the gap narrows at senior levels. DataOps and MLOps specialists earn 15-30% more than single-domain engineers.
Top paying cities
San Francisco leads at $185K-$220K (+15-25% above national average). New York follows at $175K-$210K. Seattle at $170K-$205K. Boston, Austin, and Denver cluster at $155K-$185K. Fully remote MLOps roles pay $119K-$160K — a 10-26% discount versus top metro areas, but still competitive.
The LLMOps evolution — skills that command premium pay
The highest-premium MLOps skills in 2026 are all related to LLM deployment and serving. LLM deployment and fine-tuning (vLLM, TensorRT-LLM, Triton Inference Server) adds $15K-$30K+ each. GPU cluster management and inference cost optimization is equally valuable. RAG pipeline engineering — vector search infrastructure and open-source model fine-tuning — commands similar premiums.
- LLM serving infrastructure (vLLM, TensorRT-LLM, Triton) — $15K-$30K+ premium. The highest-value MLOps skill in 2026.
- GPU cluster management and inference cost optimization — $15K-$30K premium. As AI workloads scale, managing GPU costs is critical.
- RAG pipeline engineering — $15K-$30K premium. Present in nearly every enterprise AI product.
- Feature store design and management — $10K-$20K premium. Feast, Tecton, and custom solutions.
- Experiment tracking and model registry (MLflow, Weights & Biases) — $10K-$15K premium. Core MLOps infrastructure.
- Kubeflow and ML pipeline orchestration — $10K-$15K premium. Kubernetes-native ML workflows.
- Contract rates for senior MLOps: $85-$130 per hour.
How to maximize your MLOps salary
- Specialize in LLM serving infrastructure — vLLM, TensorRT-LLM, and Triton are the highest-premium skills, adding $15K-$30K+ each.
- Learn GPU cluster management — inference cost optimization is the business-critical skill that separates $150K MLOps from $200K+ MLOps.
- Bridge ML and DevOps — MLOps earns 15-25% more than DevOps because of specialized ML knowledge. If you're a DevOps engineer, adding ML pipeline skills is the most efficient path to a raise.
- Target top-tier AI companies — Anyscale ($270K), Google ($260K), Two Sigma ($240K) pay dramatically more than average.
- Consider the ML Engineer path for peak compensation — pure ML Engineers earn slightly more at senior levels, and the skills overlap significantly.