ML engineers earn more than data engineers at every level. The median base salary gap runs $35,000–$57,000, widening to $100,000+ in total comp at FAANG and AI-native companies.
The gap is narrowest at Google and non-tech enterprises, and widest at OpenAI and Anthropic, where equity-heavy packages push ML engineer total comp above $500,000.
For data engineers considering a switch, the financial case is strong. Pipeline architecture, Python fluency, and cloud experience are the exact foundations ML engineering is built on.
If you have been researching the data engineer vs machine learning engineer salary question, you already know the headline answer: ML engineers earn more. But the headline number only tells part of the story. The gap between these two roles varies significantly based on seniority, company tier, and location, and in some cases a senior data engineer at a top-tier firm earns close to what a mid-level MLE earns at the same company.
This article gives you the current numbers from the sources that matter most, along with the context you need to read them correctly.
Table of Contents
Data Engineer vs ML Engineer Salary at a Glance
Before diving into the full breakdown, here is a side-by-side view of where both roles stand in 2026.
| Role | Entry-Level (US) | Mid-Level (US) | Senior (US) | Top-Tier TC (Levels.fyi) |
|---|---|---|---|---|
| Data Engineer | ~$124,000 | ~$140,000 – $155,000 | ~$170,000 – $213,000 | $155,000 median |
| ML Engineer | ~$130,000 – $145,000 | ~$160,000 – $190,000 | ~$202,000 – $270,000 | $262,000 median |
Sources: Glassdoor, Levels.fyi, Built In. Base salary unless noted as total compensation (TC).
Data Engineer Salary in 2026
Data engineering has become one of the most reliably compensated technical roles in the industry, and the 2026 salary picture reflects a role that is both maturing and actively repricing upward.
According to Glassdoor, US data engineers earn between $103,700 and $170,729 at the 25th to 75th percentile, with top earners at the 90th percentile reaching $213,311. Built In puts the average base salary at $125,983, with total compensation including bonuses reaching $150,234.
The most commonly reported salary band on Built In is $130,000 to $140,000, which reflects the bulk of mid-level engineers at solid but non-FAANG companies. Levels.fyi, which captures total compensation more granularly, reports a median data engineer TC of $155,000 across the broader market.
How Salary Moves Across Seniority Levels
The seniority curve in data engineering is steep and fairly consistent across sources. Entry-level data engineers coming in with 0 to 2 years of experience typically land in the $85,000 to $124,000 range. Mid-level engineers with 3 to 6 years of focused experience move into $119,000 to $150,000 in base salary, according to Motion Recruitment’s 2026 salary guide.
Senior data engineers command $147,000 to $183,500 in base compensation, with total comp climbing well above that at higher-tier companies. Staff and principal engineers who own data architecture decisions and cross-functional infrastructure sit at $180,000 to $280,000 or more in total compensation.
The jump from mid-level to senior is where most engineers see the most significant single compensation move, because it is the point where you shift from executing established patterns to designing the systems themselves. Engineers who can architect reliable, scalable pipelines from scratch rather than maintain existing ones represent a genuinely smaller talent pool, and the market prices that distinction clearly.
What Skills and Tools Move the Number
The tools that correlate most reliably with higher compensation are the ones powering the modern production data stack. Apache Spark and Databricks command a consistent premium, as Spark now runs data infrastructure at over 60 percent of Fortune 500 companies.
dbt, now used by more than 60,000 organizations, signals that an engineer understands the full analytics engineering layer, not just pipeline construction. Engineers fluent across transformation, orchestration, and cloud-native compute consistently outpace those who specialize in only one layer.
The emerging category with the strongest salary upside is the AI-adjacent data engineer. Job postings are now explicitly asking for engineers who can build real-time pipelines, feature stores, and governance infrastructure that supports production AI at scale. Industry context matters too. Fintech and healthcare roles, where pipelines are tied to revenue or compliance, pay 10 to 15 percent above comparable enterprise positions.
Machine Learning Engineer Salary in 2026
The MLE salary has been repriced sharply upward over the past two years. Glassdoor puts the current US range at $128,839 to $202,146 at the 25th to 75th percentile, with an average of $160,347. Levels.fyi, which captures full total compensation including equity, reports an average machine learning engineer salary of $245,000 across the US market.
The seniority curve in ML engineering is steeper than most engineering roles because the gap between what a junior and a staff-level engineer can independently own in production is substantial.
| Level | Base Salary (Glassdoor) | Notes |
|---|---|---|
| Entry Level (0-2 yrs) | $120,000 – $145,000 | Model training, supervised projects |
| Mid Level (2-5 yrs) | $160,000 – $190,000 | Owns specific model pipelines |
| Senior (5+ yrs) | $202,000 – $270,000 | End-to-end production ownership |
| Staff / Principal | $270,000+ | Architecture, cross-team scope |
Source: Glassdoor, Levels.fyi
At FAANG companies, the numbers shift considerably higher, and total compensation including equity becomes the more meaningful figure:
| Company | Level Range | TC Range | Median TC |
|---|---|---|---|
| Amazon | L4 – L6 | $176K – $399K | $265K |
| L3 – L7 | $199K – $743K | $290K | |
| Apple | ICT2 – ICT6 | $190K – $528K | $335K |
Source: Levels.fyi
What Skills Move the Number
Specialization is the single biggest compensation lever within ML engineering. Engineers working on LLMs, computer vision, or production recommendation systems operate in a narrower talent pool, and compensation reflects that. MLOps proficiency, specifically the ability to own model deployment, versioning, automated retraining, and production monitoring, is the clearest differentiator between mid-level and senior compensation bands on Glassdoor’s skill-premium data.
Framework fluency in PyTorch and TensorFlow is now table stakes rather than a premium signal. What moves the number at the top of the market is cloud-native model-serving experience on AWS SageMaker, Google Vertex AI, or Azure ML at production scale.
Is Demand for ML Engineers Growing?
Demand is accelerating, not plateauing. The Statista ML engineering market estimate puts the sector at $113.10 billion in 2025 and projects growth to $503.40 billion by 2030, a CAGR that reflects sustained enterprise AI investment well beyond the current cycle. The BLS projects data scientists to grow 34 percent from 2024 to 2034, the fourth fastest-growing occupation in the entire US economy, while software developers are projected to grow 17.9 percent between 2023 and 2033.
The supply constraint is structural. The engineers who can move a model from a notebook to a reliable production system with monitoring, retraining pipelines, and low-latency inference represent a small subset of the total ML talent pool. That bottleneck is what drove the $35,000 single-year salary jump between 2023 and 2024, and it has not been resolved.
How Large Is the Salary Gap?
At the median market rate, the base salary gap between ML engineers and data engineers sits between $35,000 and $57,000. Built In’s figures show a data engineer base salary of $125,983 versus a machine learning engineer base salary of $165,000 to $183,000, depending on the source.
That represents a 30 to 45 percent premium for ML engineering at the middle of the market.
The Gap at the Broad Market Level
Looking at Glassdoor data, which captures the widest cross-section of companies and geographies, machine learning engineers earn more at every percentile:
| Percentile | Data Engineer | ML Engineer | Gap |
|---|---|---|---|
| 25th percentile | $103,700 | $128,839 | +$25,139 |
| 75th percentile | $170,729 | $202,146 | +$31,417 |
| 90th percentile | $213,311 | $316,000+ | +$100,000+ |
Source: Glassdoor
The Gap Widens at the Top of the Market
When total compensation includes equity, the picture shifts considerably. Levels.fyi data shows a median data engineer total compensation of $155,000 versus a median machine learning engineer total compensation of $262,000 across the US market. That is a $107,000 difference at the median, driven primarily by how equity-heavy machine learning engineer packages tend to be at companies investing heavily in AI.
At specific companies, the gap varies significantly. Google’s median data engineer total compensation is $276,000, while their median ML engineer total compensation is $290,000, a much narrower $14,000 difference that reflects Google’s exceptional baseline data engineer compensation. Amazon’s gap is larger, with data engineer median salary of $216,000 versus ML engineer median salary of $265,000, a $49,000 difference at that specific company.
| Company | Data Engineer (Median TC) | ML Engineer (Median TC) | Gap |
|---|---|---|---|
| Amazon | $216,000 | $265,000 | +$49,000 |
| $276,000 | $290,000 | +$14,000 | |
| Apple | N/A | $335,000 | N/A |
Salary Across Seniority Levels
The gap does not stay constant as careers progress. It tends to widen at senior and staff levels, where machine learning engineers are expected to own production systems with direct revenue impact, and that accountability is priced in at a premium that data engineering roles do not always match.
| Career Stage | Data Engineer Salary | ML Engineer Salary | Gap |
|---|---|---|---|
| Entry Level | $85,000 – $124,000 | $120,000 – $145,000 | ~$20,000 – $30,000 |
| Mid Level | $130,000 – $155,000 | $160,000 – $190,000 | ~$30,000 – $40,000 |
| Senior Level | $170,000 – $213,000 | $202,000 – $270,000 | ~$40,000 – $60,000 |
| Staff / Principal | $200,000 – $280,000 | $270,000 – $400,000+ | $70,000 – $120,000+ |
Source: Glassdoor, Levels.fyi
At the entry level, the gap is real but not large. Both roles are compensated competitively out of college or from a bootcamp-style transition. The divergence becomes financially significant at the senior level, and by the time you reach staff or principal, the total compensation gap can exceed $100,000 at companies that are actively scaling AI infrastructure.
Where the Gap Narrows
There are two conditions under which the gap narrows to near-parity. The first is Google, as noted above. The second is at non-tech enterprises, including banks, healthcare systems, and large retailers, where machine learning engineering talent is valued but not yet priced at the same premium as in pure-play tech. A senior data engineer at a top-tier financial institution can earn $160,000 to $200,000, which sits close to what a senior ML engineer earns at the same type of organization.
Salary Based on Location
Geography still moves compensation for both roles, though the degree to which it matters has shifted since remote work became normalized. The highest-paying markets pay significantly more in raw numbers, but after accounting for taxes and cost of living, the real gap between locations is narrower than headlines suggest.
| City | Data Engineer Median Base | ML Engineer Median Base | Notable Factor |
|---|---|---|---|
| San Francisco | ~$171,000 | ~$195,000+ | Highest raw salaries, high cost of living |
| Seattle | ~$114,000 | ~$150,000 | No state income tax; effective value higher than raw figures suggest |
| New York City | ~$140,000 | ~$175,000 | Strong fintech and AI startup concentration |
| Remote (US) | ~$125,000 | ~$155,000 | MLE remote roles hold a premium over DE remote roles |
Source: Glassdoor, Levels.fyi
Seattle’s numbers deserve a specific note. A $175,000 Seattle package is effectively competitive with a $195,000 San Francisco offer after accounting for Washington’s lack of state income tax and lower cost of living. Remote machine learning engineer roles also command higher rates than equivalent remote data engineer positions, because the pool of qualified remote-eligible MLEs who can own production systems independently is genuinely thinner.
Salary by Company Tier
Company tier is arguably more predictive of your actual compensation than role title or location alone. The same job title can pay $140,000 at one organization and $290,000 at another, and the difference is almost entirely explained by employer category.
FAANG and Tier-One Tech
| Company | Data Engineer (Median TC) | ML Engineer (Median TC) | Gap |
|---|---|---|---|
| $276,000 | $290,000 | +$14,000 | |
| Amazon | $216,000 | $265,000 | +$49,000 |
| Apple | N/A | $335,000 | N/A |
| Microsoft | $192,000 | $243,000 | +$51,000 |
Source: Levels.fyi
Google compensates data engineers exceptionally well relative to the rest of the market, which is why the gap there narrows to just $14,000 in total compensation. Amazon and Microsoft show a more typical pattern, where the machine learning engineering premium sits between $49,000 and $51,000. Apple’s machine learning engineer median of $335,000 is among the highest recorded for any engineering role at any major company, reflecting both Apple’s equity-heavy compensation structure and the premium it places on on-device and production machine learning.
Mid-Tier Tech and Well-Funded Startups
At Series B and growth-stage startups and mid-tier tech companies, the directional gap holds but is smaller in magnitude. A machine learning engineer at this tier might earn $170,000 to $220,000 in total compensation, versus a data engineer earning $140,000 to $175,000 at the same company. The raw dollar gap is $30,000 to $50,000, which is meaningful but well below the FAANG-level difference. Equity can swing the actual outcome significantly at early-stage companies, and the base salary gap between the two roles becomes less important than the equity terms and vesting schedule.
AI-Native Companies
This is where the salary picture changes entirely. Companies whose core product is AI, including OpenAI, Anthropic, xAI, and Nvidia, are operating in a compensation tier of their own. They are competing for a small pool of engineers who can build and scale frontier AI systems, and their packages reflect that intensity.
| Company | ML Engineer Level Range | Total Compensation Range | Median Total Comp |
|---|---|---|---|
| OpenAI | L2 – L6 | $249K – $1.24M+ | $555K |
| Anthropic | Senior – Lead | $550K – $759K+ | $570K |
| Nvidia | IC1 – IC4 | $205K – $331K+ | $267K |
The important caveat with AI-native companies is that these packages are heavily equity-dependent. Anthropic’s $570,000 median in base-equivalent terms is closer to $300,000 in guaranteed cash. Engineers evaluating these offers need to read the equity terms carefully rather than anchoring to the total compensation headline.
Non-Tech Enterprise
At banks, insurance companies, healthcare systems, and large retailers, the machine learning engineering premium exists but is the smallest of any employer tier. Senior data engineers at top-tier financial institutions regularly earn $160,000 to $200,000, which sits close to the $165,000 to $210,000 range a senior machine learning engineer commands at the same organization. For data engineers at enterprise companies, the financial case for transitioning to ML engineering is real but less dramatic than FAANG comparisons suggest.
| Company Tier | Data Engineer Salary Range | ML Engineer Salary Range | Typical Gap |
|---|---|---|---|
| FAANG / Tier-One Tech | $155K – $276K TC | $262K – $335K TC | $50K – $100K+ |
| Mid-Tier Tech / Startups | $140K – $175K | $170K – $220K | $30K – $50K |
| Non-Tech Enterprise | $130K – $180K | $145K – $210K | $15K – $30K |
Why ML Engineers Earn More
The salary premium for an ML engineer is not arbitrary. There are structural reasons the market prices ML engineering higher, and understanding them clarifies what you are actually buying into when you make the transition.
Production ownership is the primary driver. ML engineers do not just build models; they deploy them, monitor them, debug them when they start drifting, and own the systems that serve predictions at scale across potentially millions of requests. That end-to-end accountability across data, model, infrastructure, and reliability is rarer than any single piece of it.
Software engineering depth is the second factor. ML engineers are expected to write production-grade code, design scalable model-serving infrastructure, and reason about latency, throughput, and system reliability in ways that most data engineers working in batch pipelines rarely need to. The skill overlap with senior software engineers is significant, and the comp reflects that overlap.
Specialization creates additional leverage. An ML engineer with deep expertise in large language models, production recommendation systems, or computer vision operates in a genuinely constrained talent pool. There are fewer of them, the ramp time to reach that level is longer, and companies price that scarcity accordingly.
The AI investment cycle is still in a phase where demand is outrunning supply. Enterprise AI spending continues to accelerate, and the bottleneck at most organizations is not ideas or data. It is engineers who can take a model from concept to a reliable production system that the business can depend on.
Does the Salary Gap Justify a Transition?
For most data engineers evaluating whether they want to transition to ML engineering, the financial case is straightforward. The salary gap is large enough to be material, and data engineers are better positioned than almost any other role to bridge it efficiently.
The skills you already have as a data engineer, specifically pipeline architecture, data infrastructure, Python fluency, and cloud platform experience, are the exact foundations that ML engineering is built on. You are not starting from scratch. What you would need to add includes ML fundamentals, model training and evaluation workflows, and MLOps tooling for deployment and monitoring.
Most data engineers with a structured preparation approach can realistically reach MLE-level compensation within 6 to 9 months of making the transition, depending on their starting ML knowledge and how deliberately they pursue the right opportunities.
The key is not just learning the skills but demonstrating them in a way that hiring managers at target companies recognize. That means building projects that reflect production thinking, not just notebook experiments, and preparing specifically for MLE interview loops that combine system design, ML fundamentals, and software engineering.
For the full career transition roadmap, read our complete guide on transitioning from data engineer to machine learning engineer].
FAQs: Data Engineer vs Machine Learning Engineer Salary
1. Do machine learning engineers make more than data engineers?
Yes, consistently and significantly. At the median US market rate, ML engineers earn $35,000 to $57,000 more in base salary than data engineers. At FAANG and top-tier companies, the Levels.fyi total compensation medians show a gap of over $100,000.
2. What is the average data engineer salary in 2026?
According to Built In, the average data engineer base salary in the US is $125,983, with total compensation including bonuses reaching $150,234. Glassdoor’s typical range runs from $103,700 to $170,729 at the 25th to 75th percentile.
3. What is the average ML engineer salary in 2026?
Glassdoor puts the average at $160,347, with a range of $128,839 to $202,146 at the 25th to 75th percentile. Levels.fyi reports a higher average of $245,000 when total compensation including equity is factored in.
4. Which companies pay ML engineers the most?
Apple, Google, and Amazon are consistently among the top payers for ML engineers. Apple’s median ML engineer total comp on Levels.fyi is $335,000. Google’s median is $290,000. Amazon’s median is $265,000. AI-native companies like OpenAI (median $555,000) and Anthropic (median $570,000) are paying at the very top of the market, though those figures are heavily equity-dependent.
5. Is transitioning from data engineer to ML engineer worth it financially?
For most data engineers, yes. The salary gap is substantial and well-documented. Data engineers have a structural advantage in making the transition because they already own the data infrastructure skills that ML engineers depend on. A realistic timeline to reach MLE-level compensation after a focused transition is 6 to 24 months with proper preparation, depending on starting ML knowledge and target company tier.
