Machine learning engineer salaries in the US range from $70,000 to over $318,000 annually, with a median base salary around $155,000 and average total compensation (including bonus and equity) reaching $212,022, according to Built In’s 2026 data across thousands of reported salaries. The spread is wide because the role spans junior engineers at growth-stage startups all the way to staff-level researchers at hyperscalers where total comp can exceed $600,000.
This is not a role where “average salary” tells you much on its own. Where you work, what level you hold, and which skills you bring to the table move the number by more than $100,000. A mid-career ML engineer in San Francisco writing production model pipelines earns substantially more than someone in the same role at a regional company in the Midwest. Understanding those levers is how you benchmark your own compensation accurately.
For the SEO and content angle: machine learning engineer salary is also one of the more contested salary queries in AI search. ChatGPT, Perplexity, and Google AI Overviews all generate direct salary answers from publisher data, making it a useful case study in how structured salary content gets pulled into AI citations.
What Is the Average Machine Learning Engineer Salary in 2026?
The average base salary for a machine learning engineer in the US is $162,080, with a median of $155,000 and total compensation (base plus bonus and equity) averaging $212,022. The full range runs from $70,000 at the low end to $318,000 at the high end, based on Built In’s 2026 salary data. PayScale, drawing on a different sample of 1,127 profiles updated April 2026, reports a lower median of $125,000, reflecting its inclusion of more traditional industry roles outside Big Tech.
The gap between PayScale and Built In reflects sample bias: salary aggregators that over-index toward FAANG and tech-forward companies produce higher figures. Neither number is wrong; they answer different questions. If you work at a big tech company in a major city, the Built In figure is the more relevant benchmark. If you’re at a manufacturing or healthcare company deploying ML internally, PayScale’s sample is closer to your market.
Salary Percentile Breakdown (Base Pay, US)
| Percentile | Annual Base Salary |
|---|---|
| 10th | $94,750 |
| 25th | $131,875 |
| 50th (median) | $155,000 |
| 75th | $255,625 |
| 90th | $292,750 |
Source: Built In, data reviewed May 26, 2026.
Machine Learning Engineer Salary by Experience Level
Entry-level ML engineers earn around $120,571 on average, rising to $194,702 for those with seven or more years of experience, according to Built In’s 2026 data. PayScale’s sample shows a similar direction: entry-level sits at $102,174 (18% below the PayScale average), while late-career engineers run 31% above average and experienced engineers 37% above.
The jump from mid-career to senior is typically where compensation accelerates most. Senior engineers start taking on system design ownership, mentorship, and cross-functional work with product and data science teams, all of which are scarcer than raw coding ability and priced accordingly.
For AI and ML specifically, levels.fyi’s Q3 2025 data found that AI-focused engineers earn a premium over non-AI engineers at every experience level. At the senior level, that premium was 14.2%; at staff level it was 18.7%. Concrete examples from the levels.fyi data: LinkedIn AI engineers at entry level were earning $288,050 total compensation versus $225,000 for non-AI engineers in the same band. At staff level at Intuit, AI engineers hit approximately $917,000 total compensation versus $515,000 for non-AI staff engineers.
Experience Level Snapshot
| Experience Stage | Approximate Base Salary |
|---|---|
| Entry level (0-1 year) | $102,000-$120,000 |
| Early career (1-4 years) | $123,000-$135,000 |
| Mid career (4-7 years) | $145,000-$170,000 |
| Senior / late career (7+ years) | $175,000-$195,000+ |
Note: Figures approximate ranges derived from Built In and PayScale 2026 data; total compensation at top tech companies will be materially higher.
Machine Learning Engineer Salary by Location
Geography is one of the biggest levers in ML engineering pay. San Francisco consistently leads: Indeed reports an average of $222,174 for the San Francisco metro, while Built In puts San Francisco at $207,474, about 26% above the national average. Mountain View ($216,648), Seattle ($202,397), and New York ($198,884) round out the top metro areas according to Indeed’s 2026 data.
Remote ML engineering also pays well: Built In shows remote compensation averaging $195,475, about 21% above the national average. This reflects the reality that most remote ML engineering jobs are posted by the same tech-forward companies that anchor the high-paying metros.
Top-Paying Cities for ML Engineers
| City | Average Annual Salary |
|---|---|
| San Francisco, CA | $207,474-$222,174 |
| Mountain View, CA | $216,648 |
| Seattle, WA | $182,182-$202,397 |
| New York, NY | $166,201-$198,884 |
| San Jose, CA | $190,490 |
| Austin, TX | $201,340 |
| Los Angeles, CA | $194,960 |
| Remote | $195,475 |
Sources: Built In 2026, Indeed 2026.
Machine Learning Engineer Salary by Company
Top-paying individual employers tend to be financial firms and high-revenue tech companies rather than the most famous tech brands. Indeed’s data shows D.E. Shaw topping the list at $392,500 average, followed by Susquehanna International Group ($312,000) and Grammarly ($310,938).
Among volume employers, H1B petition data from 2024 (883 approved applications) shows Apple paying a median of $185,779 across 208 positions, Meta at $210,572 across 42 positions, and Adobe at $194,000 across 108 positions. TikTok had a median of $196,977 across 32 positions and ByteDance hit $228,900 across 26 positions.
The H1B data is a useful cross-check because it captures formal Labor Condition Application filings, which are actual committed salary floors rather than self-reported figures.
Notable Company Benchmarks
| Company | Median / Average MLE Salary |
|---|---|
| D.E. Shaw | $392,500 (Indeed) |
| Susquehanna International Group | $312,000 (Indeed) |
| Meta (H1B) | $210,572 median (2024) |
| ByteDance (H1B) | $228,900 median (2024) |
| Adobe (H1B) | $194,000 median (2024) |
| Apple (H1B) | $185,779 median (2024) |
| TikTok (H1B) | $196,977 median (2024) |
AI Engineer Premium: What ML Engineers Working on LLMs Earn
The shift toward large language model work has created a measurable earnings gap between ML engineers on traditional model pipelines and those working directly on AI products. Levels.fyi’s Q3 2025 report shows AI engineer total compensation settled in the $260,000-$269,000 range in Q3 2025, up from a January 2025 low of $228,500 and below the March 2024 peak of $295,000, reflecting both post-peak cooling and continued demand.
The AI engineer premium versus non-AI engineers ranged from 6.2% at entry level to 18.7% at staff level in Q3 2025 data. In dollar terms this is significant: a staff AI engineer at a well-funded company earns meaningfully more than a staff engineer running non-AI product work.
This trend matters for career planning. ML engineers who develop depth in transformer architectures, fine-tuning, RLHF, and inference optimization are commanding top-of-market packages. Those staying in classical ML (recommendation systems, tabular models, traditional NLP) have not seen the same step-change in compensation.
Skills That Move Machine Learning Engineer Salaries
Not all ML engineering skills pay equally. Based on verified data from Built In’s 2026 salary data and PayScale’s skill premium analysis, these technical areas command above-average compensation:
- Deep learning and neural networks (especially transformer architectures)
- Python (effectively table stakes, but depth in ML-specific libraries like PyTorch and JAX matters)
- Natural language processing (high demand for LLM-adjacent work)
- Computer vision (steady demand in robotics, autonomous systems, medical imaging)
- MLOps and production deployment (AWS, Google Cloud, Kubernetes for model serving)
- Big Data and distributed training (Spark, Ray for large-scale workloads)
Cloud infrastructure skills (AWS, GCP, Azure) are increasingly baseline requirements rather than premiums, but certified proficiency in ML-specific cloud services (SageMaker, Vertex AI) still adds value.
How AI Search Handles Machine Learning Engineer Salary Queries
Machine learning engineer salary is a high-intent informational query that AI engines (ChatGPT, Perplexity, Google AI Overviews) regularly answer with pulled salary figures. Understanding how this works is relevant if you publish salary content, because the citation pattern reveals what gets surfaced.
Google AI Overviews tend to pull salary ranges from structured data (schema.org Occupation markup with MonetaryAmountDistribution) combined with high-authority sources. Built In’s salary page works precisely because it uses that schema format: median, percentile10, percentile25, percentile75, and percentile90 are machine-readable fields.
Perplexity typically cites 3-5 sources per salary query, ranking by freshness and domain authority. Pages that update their salary data regularly and include explicit year/date references rank more consistently. Outdated figures get replaced.
ChatGPT (when web-enabled) draws from the same pool of high-ranking pages but tends to synthesize a single “typical” figure rather than showing ranges. This means your median figure is the most-cited number, not your range.
To rank in both Google and AI answer engines for salary queries, pages need: verified, dated figures from named sources; schema markup that makes those numbers machine-readable; and regular updates as data refreshes. A page that was accurate in 2023 but hasn’t been updated in 18 months will lose ground to fresher competitors. You can track whether AI engines are citing your salary pages using tools like Fokal, which monitors brand and page visibility across ChatGPT, Perplexity, and Google AI Overviews.
For a broader look at how AI search citation mechanics work, see Fokal’s guide to how AI engines choose brands and the AI citation framework.
Machine Learning Engineer vs. Related AI Roles: Salary Comparison
Machine learning engineer compensation sits in the mid-to-high range among AI-adjacent roles, higher than data scientist but typically below AI researcher at frontier labs.
- AI Engineer (broad, includes ML engineering): $260,000-$269,000 settled range (levels.fyi Q3 2025)
- Machine Learning Engineer: $155,000 median base, $212,022 average total comp (Built In 2026)
- Data Scientist: lower median than ML engineer in most surveys; more variable
- Prompt Engineer: a newer, narrower role. See Fokal’s prompt engineer salary guide
- AI Product Manager: management track, different comp structure. See AI product manager salary
- Anthropic/OpenAI staff: frontier lab compensation reported significantly above market. See Anthropic salary data and OpenAI salary data
The full landscape of AI role compensation is covered in Fokal’s AI salaries hub.
What Affects Machine Learning Engineer Salary Most?
Company type matters more than job title. An ML engineer at a FAANG or top-tier AI lab earns two to three times what the same title pays at a regional company or government contractor. Levels.fyi’s data shows this gap persisting across all experience levels.
Equity (RSUs) is where senior ML engineering compensation diverges most sharply. At large tech companies, annual RSU grants for senior and staff engineers often exceed base salary. Total compensation figures above $300,000 are almost always equity-heavy; the base salary component alone rarely clears $250,000 even at top companies.
The clearest path to top-of-market compensation combines: (1) depth in LLM/generative AI infrastructure, (2) a track record of shipping models at production scale, (3) location in a top-paying metro or remote work for a top-paying company, and (4) negotiation using verified comp data, precisely what platforms like levels.fyi exist to provide.
Industry segmentation also matters. Financial services firms (D.E. Shaw, SIG, hedge funds) consistently pay above Big Tech for ML talent because the returns on alpha-generating models are direct and measurable. Healthcare ML and autonomous vehicle companies also pay at the top end, driven by regulatory complexity and talent scarcity.