ML Engineer Salary Calculator

Understanding ML engineer compensation is critical whether you're negotiating a job offer, planning your career growth, or assessing market rates for hiring. Our ML Engineer Salary Calculator provides ESTIMATED salary ranges based on experience level, location, and company size—key factors that influence compensation in 2024.

Machine Learning engineers are among the highest-paid roles in tech, but salaries vary significantly. An entry-level ML engineer (0-2 years of experience) might earn between $110k-$150k total compensation in major U.S. tech hubs, while senior ML engineers (6+ years) often surpass $250k-$400k at top-tier companies, according to public data from Levels.fyi, Glassdoor, and Payscale. Outside the U.S., salaries adjust for local market conditions (e.g., London ML engineers earn ~£80k-£140k, while Zurich pays CHF 120k-CHF 200k).

Location is a major driver: San Francisco offers the highest U.S. salaries (with a multiplier of ~1x as baseline), followed by New York (~1.2x due to higher salaries but also higher taxes and living costs). Remote roles often pay U.S. averages (~0.6x) unless the company offers location-based adjustments. Company size also plays a role—large enterprises and FAANG companies typically pay 15-30% more than startups for comparable roles.

This tool uses ESTIMATED multipliers derived from aggregated salary reports. For global benchmarks, U.S. Bureau of Labor Statistics and LinkedIn Talent Insights provide additional context on broader labor trends. Keep in mind that individual offers may vary based on specialization (e.g., MLOps vs. research), negotiation, and benefit packages (e.g., bonuses, 401k matches, or stock refreshers).

Use this calculator as a starting point for your salary research, and pair it with real-world data from sites like Levels.fyi or Blind for specific companies.

How It Works

This calculator estimates ML engineer salaries by combining:

• Experience level: Multipliers based on career stage (entry-level to principal).
• Location: Geographic adjustment factors accounting for cost-of-living and market demand.
• Company size: Scaling factors for startup vs. enterprise compensation bands.
• Equity: Optional RSU projections as a percentage of base salary (only relevant for startups/tech companies).

The formula applies these factors sequentially to compute an ESTIMATED base salary and total compensation. Adjust inputs to see how changes (e.g., relocating from San Francisco to Austin) impact earnings.

Methodology Note

This calculator provides ESTIMATES using public salary data from the following sources:

• Levels.fyi: Crowdsourced total compensation data for ML/AI roles at major tech companies (FAANG, startups).
• Glassdoor/Payscale: Aggregated salary ranges for ML engineers across experience levels and locations.
• LinkedIn Talent Insights: Market demand analysis for ML roles, including median salaries by region.
• U.S. Bureau of Labor Statistics: Occupational wage data for related roles (e.g., software developers, data scientists) as a baseline.

Compensation varies by industry (e.g., fintech pays ~20% more than non-profits), specialization (ML research vs. applied ML), and individual negotiation. Equity estimates assume liquidity events (e.g., IPO, acquisition) and are highly speculative—most startups fail, and even at successful companies, RSUs may vest over 4 years with a 1-year cliff.

Key limitations:

• Data represents U.S.-centric benchmarks (though international multipliers are included).
• Does not account for bonuses, signing bonuses, or other perks (e.g., annual bonuses may add 10-25% for senior roles).
• Equity projections are approximations; actual value depends on company performance.
• Remote pay policies vary (some companies adjust for location, others pay a flat U.S. rate).

For personalized advice, consult multiple sources and compare with recent job offers in your specific market.

Frequently Asked Questions