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  1. Home/
  2. Salary/
  3. MLOps Engineer

MLOps Engineer Salary.

Across 30 U.S. cities.

$160,000

national median salary

$120,000 to $210,000. Last updated April 2026.

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Highest Paying

$225,000

San Jose, CA

Best Purchasing Power

$167,000

Phoenix, AZ

Lowest Paying

$138,000

Detroit, MI

Salary data sourced from SEC filings, H-1B Labor Condition Applications (DOL), Bureau of Labor Statistics Occupational Employment and Wage Statistics, and aggregated job postings across 50+ platforms. Ranges reflect 25th to 75th percentile for full-time positions. Cost-of-living adjustments use Bureau of Economic Analysis Regional Price Parities (2025 index). Last updated April 2026.

The average MLOps Engineer salary in the United States is $160,000 in 2026, with the full range spanning $120,000 at the 25th percentile to $210,000 at the 75th. San Jose pays the most at $225,000, while Phoenix offers the best purchasing power after cost-of-living adjustments. Experience with ML pipeline orchestration (Kubeflow, MLflow, Vertex AI), model monitoring in production, and infrastructure automation are the key salary levers.

MLOps Engineer salary by city

What you should know

Experience with ML pipeline orchestration (Kubeflow, MLflow, Vertex AI), model monitoring in production, and infrastructure automation are the key salary levers. Engineers who can reduce model deployment time from weeks to hours earn premiums. Understanding both the ML lifecycle and cloud infrastructure deeply is what separates mid level from senior compensation.

Junior MLOps engineers start at $105,000 to $135,000, reaching mid level at $140,000 to $180,000 in two to three years. Senior MLOps engineers earn $180,000 to $240,000. Staff level ML platform engineers at top companies can exceed $300,000 in total compensation, with a path into ML infrastructure leadership.

Equity at AI companies adds $20,000 to $90,000+ annually. Bonuses of 10 to 20% are typical. On call compensation is common since MLOps engineers maintain production ML systems. Benefits frequently include cloud certification sponsorship, training budgets, and flexible work arrangements.

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