ML Engineer vs Data Scientist Salary Tracker
Compare ML engineer vs data scientist salaries with real-time estimates. Adjust for experience, location, and role to benchmark compensation trends.
Understanding the salary landscape for machine learning engineers vs data scientists is critical for career planning, job negotiations, or hiring talent. While both roles operate at the intersection of AI and data, their compensation trends often diverge due to differences in demand, specialization, and technical depth. This ML engineer vs data scientist salary tracker provides an interactive tool to compare estimated compensation across roles, experience levels, locations, and company sizes.
Historically, ML engineers command higher salaries than data scientists, reflecting their focus on production-grade AI systems, software engineering rigors, and deployment pipelines. According to Levels.fyi and Glassdoor, the median total compensation for ML engineers at top tech firms ranges between $180,000 to $300,000 in major U.S. tech hubs, while data scientists typically earn $150,000 to $250,000 in similar roles. These estimates vary widely based on factors like company size, equity grants, and geographic location.
The Bureau of Labor Statistics (BLS) aggregates broader trends, showing that roles related to "Computer and Information Research Scientists" (which includes ML engineers) grew 22% from 2020 to 2030—nearly triple the average job growth rate. Meanwhile, "Data Scientists" grew 36% in the same period, driven by demand for analytics but lower median salaries. This tracker helps you navigate these nuances by adjusting for role-specific multipliers derived from public compensation reports.
Use the inputs below to model your own compensation scenario. Inputs like base salary, bonus percentage, equity value, and location multiplier allow granular comparisons between ML engineering and data science roles. All data are labeled as ESTIMATES and sourced from Levels.fyi, Glassdoor, Payscale, and LinkedIn Talent Insights—refer to the Methodology Note for details.
How It Works
This ML engineer vs data scientist salary tracker calculates total compensation by applying multipliers to a base salary input. The multipliers reflect role-specific, experience-based, and geographic trends sourced from public compensation reports. Here’s the step-by-step logic:
- Role Type Multiplier: ML engineers typically earn 5-12% more than data scientists due to higher demand for production-grade AI systems. Specialized data scientists (e.g., in NLP or CV) may close this gap.
- Experience Multiplier: Adjusts base salary linearly; entry-level roles (0-2 years) start at ~70% of mid-level (3-5 years), while senior/principal roles (10+ years) command up to 160%.
- Location Multiplier: Cost-of-living adjustments for major tech hubs (e.g., San Francisco, New York) versus remote or lower COL areas.
- Company Size Multiplier: Enterprise companies offer 20-40% higher compensation than startups, reflecting equity packages and higher base salaries.
- Bonus and Equity: These are added to the adjusted base salary to compute total compensation.
Methodology Note
All figures in this ML engineer vs data scientist salary tracker are ESTIMATES, not precise guarantees. The tool relies on the following public data sources:
- Levels.fyi: Crowdsourced total compensation data from tech professionals, segmented by role, experience, and company.
- Glassdoor/Payscale: Self-reported salary ranges for ML engineers and data scientists, adjusted for geographic trends.
- LinkedIn Talent Insights: Aggregated hiring trends and compensation benchmarks across industries.
- Bureau of Labor Statistics (BLS): Macroeconomic data on job growth and median salaries for "Computer and Information Research Scientists" and "Data Scientists."
The multipliers in this tool are derived from median differences observed in these datasets. For example, the role type multiplier (ML engineer = 1, Data Scientist = 0.92) comes from levels.fyi’s 2023 reports showing a ~8% salary gap between these roles at similar experience levels. Equity values are annualized estimates based on typical grant structures.
No company-specific data (e.g., Google, Meta) is used. For precise benchmarks, consult verified salary surveys or professional compensation reports.
Frequently Asked Questions
On average, ML engineers earn 5-15% more than data scientists, according to Levels.fyi and Glassdoor data. This gap widens in production-heavy roles (e.g., MLOps) but narrows for specialized data scientists (e.g., in NLP or CV). Factors like equity, location, and company size can shift this range.
Rarely, but possible. Some companies (e.g., quant funds, hedge funds) pay specialized data scientists more due to niche expertise (e.g., Bayesian statistics). However, ML engineers typically out-earn data scientists in tech firms where production-grade AI deployment is critical.
All figures are ESTIMATES based on public data sources (Levels.fyi, Glassdoor, BLS). Variability depends on factors like equity, bonuses, and geographic location. For precise numbers, consult company-specific salary reports or recruiters.
Experience correlates with production impact. A senior ML engineer may lead end-to-end deployment of a model serving millions of users, while a junior data scientist might focus on exploratory analysis. Compensation scales with responsibility, which aligns closely with experience.
Location adjustments reflect cost-of-living differences. For example, a $120K salary in San Francisco (multiplier: 1.4) equates to ~$168K in purchasing power, while the same role in a low-COL area (multiplier: 0.8) would adjust to ~$96K. Tech hubs pay a premium for access to top talent.
ML engineers typically receive 10-20% bonuses, while data scientists average 5-15%. Equity can significantly alter total compensation, especially at public tech firms where grants may vest over 4 years.
Enterprise companies (>10K employees) offer 20-40% higher compensation than startups due to equity packages, higher base salaries, and larger bonuses. Startups may compensate with equity upside, but these come with higher risk.
This tool is optimized for U.S.-based salaries. International comparisons require additional adjustments for taxes, benefits, and local market rates. For non-U.S. roles, use location multipliers as rough proxies, but consult local salary surveys for accuracy.
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