AI engineers working at top US tech companies earn total compensation packages ranging from roughly $150,000 to well over $800,000 per year, depending on the company, level, and location. The wide range reflects how much equity has come to dominate compensation at frontier AI labs compared to traditional software roles. Base salary alone tells less than half the story.
The data below comes from verified compensation submissions on Levels.fyi (updated May 29, 2026) and job-posting aggregates from Indeed (updated May 11, 2026, based on 2,300+ salaries). Where “total compensation” is cited, it includes base salary, annualized stock (RSUs), and any cash bonus.
Beyond the raw numbers, there is a practical question for anyone in the AI field: does your work show up in AI search? A senior AI engineer at a company whose content never surfaces in ChatGPT or Google AI Overviews has less negotiating leverage than one whose employer is visibly cited across AI engines. The same logic applies to your own brand in the market.
What Is the Average AI Engineer Salary in the US?
The average base salary for an AI engineer in the United States is $153,038 per year, based on 2,300+ job postings on Indeed over the past 36 months (updated May 11, 2026). Total compensation, which adds equity and bonuses, raises that figure substantially at companies like Google, Meta, and OpenAI. Talent.com reports a median of $130,175 across 10,000 salaries, with a range of $101,875 for entry-level roles up to $178,780 for experienced practitioners.
The spread between the median ($130K-$153K base) and total-comp figures at elite firms is substantial. At Google, the overall median total compensation package is $330,000. At Meta it is roughly $447,500. These gaps explain why engineers actively benchmark their packages using tools like Levels.fyi rather than relying on standard job boards.
AI Engineer Salaries by Company (Levels.fyi Data, May 2026)
Company-level data reveals the biggest spread in the market. Frontier AI labs and top tech companies pay dramatically more than the market median, almost entirely through equity.
OpenAI (levels.fyi, last updated May 29, 2026):
| Level | Total Comp | Base | Annual Stock | Bonus |
|---|---|---|---|---|
| L2 | $251K | $168K | $82.5K | $625 |
| L3 | $337K | $209K | $127K | $0 |
| L4 | $608K | $262K | $346K | $0 |
| L5 | $819K | $324K | $495K | $0 |
Median total compensation at OpenAI: $555K. Highest reported package: $1,272,250.
Anthropic (levels.fyi, last updated May 29, 2026):
| Level | Total Comp | Base | Annual Stock | Bonus |
|---|---|---|---|---|
| Senior SWE | $563K | $316K | $247K | $0 |
| Lead SWE | $785K | $332K | $453K | $0 |
Median total compensation at Anthropic: $710K. Highest reported package: $920K. Both OpenAI and Anthropic pay $0 cash bonus at most levels, routing all variable compensation through equity.
Google (levels.fyi, last updated May 29, 2026):
| Level | Title | Total Comp | Base | Annual Stock | Bonus |
|---|---|---|---|---|---|
| L3 | SWE II | $216K | $164K | $33.4K | $18.9K |
| L4 | SWE III | $306K | $195K | $87K | $23.8K |
| L5 | Senior SWE | $426K | $224K | $164K | $37.7K |
| L6 | Staff SWE | $615K | $295K | $282K | $38.3K |
Overall Google median: $330K total compensation.
Meta (levels.fyi, last updated May 29, 2026):
| Level | Total Comp | Base | Annual Stock | Bonus |
|---|---|---|---|---|
| E3 (Entry) | $194K | $151K | $32.5K | $10.1K |
| E4 | $301K | $190K | $94.4K | $16.3K |
| E5 | $486K | $228K | $230K | $27.6K |
| E6 | $686K | $268K | $382K | $35.3K |
Overall Meta median: $447,500. Range: $194K (E3) to $4.36M+ (E9).
Microsoft (levels.fyi, last updated May 29, 2026):
| Level | Total Comp | Base | Annual Stock | Bonus |
|---|---|---|---|---|
| SDE (L59) | $163K | $127K | $24.4K | $11.6K |
| L60 | $191K | $141K | $29.5K | $21K |
| SDE II (L61) | $200K | $154K | $33.4K | $13.3K |
| L62 | $219K | $170K | $31.9K | $16.8K |
Overall Microsoft median: $220K.
AI Engineer Salaries by Location
Geography is the biggest non-company variable. Indeed’s 2026 data (based on 2,300+ salaries) shows the premium tech hubs command:
| City | Average Annual Salary |
|---|---|
| San Jose, CA | $204,105 |
| San Francisco, CA | $194,680 |
| New York, NY | $181,877 |
| McLean, VA | $164,679 |
| Austin, TX | $157,301 |
Talent.com’s state-level data (based on 10,000 salaries) shows California leads at $146,482, followed by Virginia ($127,123), Maryland ($123,095), Washington ($129,545), and Massachusetts ($125,000).
The Bay Area premium is partly explained by concentration of frontier AI labs, but remote work has eroded the gap. Engineers hired remotely by OpenAI or Anthropic typically earn the same base as their San Francisco counterparts.
AI Engineers vs. Machine Learning Engineers: Salary Comparison
The two titles overlap heavily but “AI engineer” skews toward applied product work (integrating LLM APIs, building pipelines, deploying models) while “machine learning engineer” historically implied more research-adjacent model training. Indeed’s 2026 data puts the gap clearly:
| Role | Average Base Salary | Data Points |
|---|---|---|
| AI engineer | $153,038 | 2,300+ salaries |
| Machine learning engineer | $187,854 | 5,200+ salaries |
The ML engineer premium ($34,000+) reflects that ML engineering roles at companies like D.E. Shaw ($392,500), Susquehanna International Group ($312,000), and Grammarly ($310,938) skew toward high-finance and scale-stage product companies with deep ML training stacks. As “AI engineer” becomes the dominant job title for LLM-focused work, the gap is likely to compress.
For more on ML engineer compensation, see the machine learning engineer salary breakdown.
What Drives AI Engineer Compensation?
Four factors account for most of the variance in AI engineer pay.
Company type and funding stage. Frontier AI labs (OpenAI, Anthropic, DeepMind, xAI) pay total comp that most companies cannot match because equity at these firms carries speculative upside. A Google L5 earns $426K. An OpenAI L5 earns $819K. The base salary difference is $100K; the equity difference is $331K.
Engineering level and scope. Moving from entry-level (L3/E3) to senior (L5/E5) roughly doubles total compensation at both Google and Meta. The jump from senior to staff or principal is often another doubling at top firms.
Specialization depth. Engineers who have shipped production LLM systems, trained or fine-tuned foundation models, or built AI infrastructure (inference optimization, distributed training) command premiums over generalist engineers who have added “AI” to their resume by calling an API.
Geographic market. Bay Area and Seattle roles pay 25-30% more than the national average even for companies with strong remote programs, because those markets compete for the same talent pool.
How AI Visibility Affects Your Career in This Field
Here is an angle most AI salary guides skip entirely.
As an AI engineer, your hireability is tied to how well your work, writing, and expertise surface when hiring managers or technical leads ask ChatGPT or Perplexity “who builds the best [LLM inference pipelines / RAG systems / AI evaluation frameworks]?” If your published work, your company’s technical blog, or your own content does not appear in those answers, you are invisible to a growing share of the recruiting market.
The same logic applies at the company level. AI engineering teams whose technical work gets cited in AI Overviews, Perplexity answers, and ChatGPT responses get inbound recruiting interest that teams without that visibility do not. This is not abstract: AI engines cite specific published technical content, named engineers, and company engineering blogs when answering “who should I talk to about [topic].”
Winning that visibility requires the same practices that get a brand cited in AI answers: structured content with clear entity signals, answer-optimized writing that AI engines can parse, and consistent publishing on topics where you want authority. Tools like Fokal track whether your content is being cited in AI answers and across Google, which lets you close the loop between what you publish and what AI engines surface.
For context on how AI engines decide which sources to cite, see how AI search works.
AI Engineer Salary Benchmarks Versus the Broader Market
A useful anchor: the wider AI salaries landscape shows how AI engineers fit relative to other AI roles.
- AI engineer average base: $153,038 (Indeed, May 2026)
- Machine learning engineer average base: $187,854 (Indeed, May 2026)
- Entry-level AI engineer (talent.com): $101,875
- Experienced AI engineer (talent.com): $178,780
- Top-of-market (OpenAI L5 total comp): $819K
The $153K average base is roughly 2-2.5x the US median individual income, reflecting how tight the labor market for verified AI engineering skills remains. See the full AI salaries hub for how this compares to other AI roles including prompt engineers and AI product managers.
Getting Accurate Compensation Data
Three sources dominate reliable AI compensation benchmarking:
Levels.fyi collects verified offer letters and pay stubs. Its data is skewed toward FAANG and high-paying startups, which means it overstates typical market comp but is the most accurate for top-tier roles. The data above comes from its verified submissions updated May 29, 2026.
Indeed salary data aggregates job postings over 36 months, which captures a broader employer set but reflects advertised rather than actual paid salaries. Its 2,300+ AI developer data points (updated May 11, 2026) are a reasonable cross-check.
Talent.com aggregates across platforms with 10,000+ salary points, giving a better median but less granularity on top-of-market packages.
For anyone negotiating: the total compensation gap between companies is large enough that base salary is often the wrong number to anchor on. An offer at $200K base with $500K in four-year equity is worth roughly $325K per year before any valuation change. An offer at $170K base with $400K in equity is worth $270K. The base salary looks closer than the packages actually are.