Data Engineer
$106,667
avg. annual salary
Ai/ml Engineer
pays more on average
Ai/ml Engineer
$110,926
avg. annual salary
As technology evolves, the demand for skilled professionals in data engineering and artificial intelligence (AI) continues to rise. Understanding the salary landscape for these two roles is critical for those considering a career change or starting anew. Data Engineers are primarily responsible for designing, constructing, and maintaining data pipelines, ensuring that organizations can effectively utilize their data. In contrast, AI/ML Engineers focus on creating algorithms, developing models, and enhancing machine learning processes to provide actionable insights from data. This article compares the salaries, benefits, and career paths for Data Engineers and AI/ML Engineers, providing you with a clearer understanding of which path might be right for you in 2025.
Salary by Experience Level
starting salary
starting salary
avg. difference (4%)
Salary Overview
In 2025, the average salary for Data Engineers is projected to be around $120,000 per year, while AI/ML Engineers can expect an average salary of approximately $135,000. This difference highlights the specialized skill sets required in machine learning and AI technologies.
Salary by Experience Level
Data Engineers typically start with an entry-level salary of around $80,000, progressing to $115,000 at mid-level positions and reaching about $150,000 at senior levels. AI/ML Engineers, starting at around $90,000, can earn $125,000 as mid-level professionals, with senior roles paying upwards of $170,000.
Benefits Comparison
Both roles offer attractive benefits, including health insurance, retirement plans, and remote work options. However, AI/ML Engineers often receive additional perks like access to cutting-edge technology and opportunities for continuous education in advanced AI frameworks.
Career Path and Job Growth
The career path for Data Engineers is robust, with opportunities in various sectors such as finance, healthcare, and tech. The job growth rate for this role is roughly 14% annually.
Conversely, AI/ML Engineers are at the forefront of technological innovation, with a staggering growth rate of 22% as industries adopt AI solutions.
Skills Required for Each Role
Data Engineers need strong programming skills in languages like Python and Java, along with knowledge of databases and ETL processes. AI/ML Engineers require a solid foundation in machine learning algorithms, statistics, and data analysis, alongside proficiency in languages like R and TensorFlow.
Detailed Salary Comparison: Data Engineer vs AI/ML Engineer (2025)
Data Engineers and AI/ML Engineers show different pay profiles in 2025. Typical U.
S. base salaries: Data Engineer median ≈ $120,000, AI/ML Engineer median ≈ $150,000.
Entry-level Data Engineers often start at $80k–$100k; entry-level AI/ML roles start at $100k–$130k. At senior levels, Data Engineers reach $160k–$220k, while senior AI/ML Engineers commonly earn $200k–$320k.
Total compensation diverges more: equity and signing bonuses lift AI/ML roles — large tech firms report total comp for mid/senior AI roles of $220k–$450k, versus $180k–$350k for comparable data engineering roles. Location matters: San Francisco and Seattle premiums add ~20–30% to base; NYC adds ~15–25%.
In short, AI/ML roles pay higher on average, but data engineering pays competitively with steady demand and slightly lower volatility. Actionable takeaway: target AI/ML for higher upside; choose data engineering for broader role availability and predictable pay growth.
Key Factors to Consider When Comparing Salaries
Compare offers beyond base pay by evaluating these specific factors:
- •Skill specialization: ML model design, deep learning, and MLOps command 10–30% higher pay than general ETL work. For example, a candidate with production ML pipelines can add $20k–$50k to offers.
- •Company type: Startups often give more equity but lower base salary; established tech firms pay higher base + RSUs. Example: 10,000 RSUs at $50 = $500k vested over 4 years (~$125k/year).
- •Location and remote: Adjust base by regional multipliers (SF +25%, NYC +20%, Austin +10%).
- •Career trajectory: AI/ML roles often lead to research or product-specialist tracks with faster salary growth.
- •Work scope: On-call and 24/7 support roles may include 5–15% extra compensation.
Actionable takeaway: calculate total comp (base + bonus + equity) and project 3-year earnings before choosing.