Federated Learning Engineer Salary.
Across 30 U.S. cities.
$188,000
national median salary
$140,000 to $248,000. Last updated April 2026.
Highest Paying
$267,000
San Jose, CA
Best Purchasing Power
$196,000
Boston, MA
Lowest Paying
$168,000
Kansas City, MO
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 Federated Learning Engineer salary in the United States is $188,000 in 2026, with the full range spanning $140,000 at the 25th percentile to $248,000 at the 75th. San Jose pays the most at $267,000, while Boston offers the best purchasing power after cost-of-living adjustments. This privacy-preserving AI specialization commands premiums driven by regulatory demand and technical complexity.
Federated Learning Engineer salary by city
What you should know
This privacy-preserving AI specialization commands premiums driven by regulatory demand and technical complexity. Engineers with production federated learning deployments across healthcare, finance, or telecommunications earn 15 to 22% more than general ML engineers. Expertise in secure aggregation protocols, differential privacy mechanisms, and communication-efficient training across distributed nodes significantly increases market value.
ML engineers or privacy engineers earning $115,000 to $155,000 specialize into federated learning at $140,000 to $248,000. Senior federated learning engineers earn $195,000 to $270,000 before advancing to Principal Privacy ML Engineer or Head of Privacy-Preserving AI at $230,000 to $300,000.
Total packages range from $220,000 to $420,000 with equity, privacy compliance bonuses, and research incentives of 12 to 22% of base. Healthcare and financial services companies offer the strongest total compensation due to strict data privacy requirements driving federated learning adoption.