The average AI engineer salary in the United States falls somewhere between $140,000 and $185,000 in base pay as of 2026, depending on which salary database you trust. Total compensation pushes well past $200,000 for mid-career engineers and regularly clears $300,000 at the senior level when you add equity and bonuses. This guide pulls from Glassdoor, Built In, Levels.fyi, PwC, the Bureau of Labor Statistics, and what we see in actual signed offer letters at KORE1 every week. We break down the real numbers by experience, specialization, city, and industry so employers can budget without guessing and candidates can walk into negotiations knowing exactly where they stand.
Every Salary Source Disagrees (Here’s Why That’s Normal)
Glassdoor says $140,678 average. Built In says $184,757. ZipRecruiter says $116,949. Levels.fyi, which pulls from 9,500+ self-reported profiles that skew heavily toward Big Tech, says $211,000 median base.
That’s a $94,000 gap between the lowest and highest number for the same job title in the same country.
Nobody’s making stuff up. ZipRecruiter casts the widest net and ends up counting a bunch of roles where somebody in HR stuck “AI” into the job posting because it looked good on LinkedIn. Glassdoor is self-reported and tends to miss the full picture on equity-heavy packages at companies like Google or Meta. Built In skews toward startups and mid-size tech companies with real engineering teams. And Levels.fyi is basically a FAANG compensation tracker that happens to include some other companies too.
What do we actually see when we place AI engineers? Signed offers, not surveys. For engineers doing genuine production AI and ML work, base salaries cluster between $155,000 and $200,000 at the mid-level. Below $140K and you’re either talking about a truly junior role, an adjacent position that isn’t really AI engineering, or a company that’s about to learn why their job posting has been open for four months.
AI Engineer Salary by Experience Level
Nobody will be shocked to hear that experience is the single biggest thing driving AI engineer pay. The part that does shock people, especially hiring managers who haven’t recruited for these roles before, is how steep the jump gets between mid-level and senior.
| Experience Level | Years | Base Salary Range | Total Comp (Est.) |
|---|---|---|---|
| Entry Level | 0–2 | $90,000 – $135,000 | $110,000 – $160,000 |
| Mid-Level | 3–5 | $140,000 – $210,000 | $170,000 – $260,000 |
| Senior | 6–9 | $180,000 – $280,000 | $220,000 – $350,000+ |
| Staff / Principal | 10+ | $250,000 – $400,000+ | $350,000 – $600,000+ |
Sources: Built In (2026), Glassdoor (Feb 2026), Levels.fyi via Exceeds AI (Jan 2026). U.S. data. Total comp includes base, bonuses, and equity.
About entry level. “Entry level” in AI engineering is kind of a joke because it doesn’t really mean entry level the way most people think of it. You’re not grabbing someone out of a bootcamp and handing them a TensorFlow tutorial. Almost everyone we see getting entry-level AI offers has a CS degree at minimum, frequently a master’s, and usually some real project work training or deploying models. In San Francisco or New York those entry-level offers routinely start at $115K to $135K in base alone. Before equity.
Mid-level is where the bidding wars happen.
MRJ Recruitment published their 2026 AI salary benchmarks and showed about 9% year-over-year growth for engineers with three to five years of hands-on ML experience. That’s the steepest climb of any experience band. Makes sense when you think about it, because these are the people who can ship production models without needing someone looking over their shoulder the whole time but they don’t yet cost what a staff engineer costs. Every company wants them. There are not enough of them. The math writes itself.
Senior engineers and above are basically designing systems, mentoring teams, making architectural decisions that affect entire product lines. We placed a senior AI engineer last quarter at $275K base with another $100K or so in equity at a company most people would recognize. Not an outlier exactly but definitely the upper end outside of FAANG. Inside FAANG, staff and principal engineers can see total comp north of $600K, but I want to be honest about the fact that most companies reading this guide are not competing at that level and shouldn’t benchmark against it. If you’re trying to land and keep senior AI talent specifically, our guide on recruiting and retaining senior AI and ML engineers goes deeper on the pay, perks, and career path side of things.
Specialization Changes Everything
The job title “AI engineer” is becoming less useful every year. What actually matters for compensation is the specific type of AI work you do. A computer vision engineer building quality inspection models for a manufacturing company and an LLM engineer fine-tuning GPT-4 on proprietary healthcare data are both AI engineers on paper but they live in completely different salary universes.
| Specialization | Mid-Level Base | Senior Base | What We’re Seeing |
|---|---|---|---|
| Machine Learning Engineer | $149K – $219K | $220K – $300K+ | Broadest bucket, ~9% YoY growth |
| LLM / Generative AI Engineer | $165K – $230K | $240K – $350K+ | Hottest demand right now, everybody wants these people |
| NLP Engineer | $155K – $220K | $225K – $320K+ | The line between NLP and LLM engineer barely exists anymore |
| Computer Vision Engineer | $150K – $215K | $220K – $310K+ | Manufacturing, autonomous vehicles, medical imaging |
| MLOps Engineer | $145K – $200K | $210K – $280K+ | Nobody talks about MLOps but everybody needs it |
| AI Research Scientist | $180K – $280K | $300K – $489K+ | PhD required, top labs have their own pay reality |
Sources: Second Talent (2026), Built In (2026), Glassdoor (2026), MRJ Recruitment (2026). U.S. base salary only.
Generative AI engineers are the headline. Every company that got past the “let’s just call the OpenAI API and see what happens” phase now needs people who can fine-tune foundation models, build retrieval-augmented generation pipelines, set up guardrails, and evaluate outputs in a way that actually makes sense for their business. Not theoretical. Production-grade. Analytics Vidhya research cited by Second Talent puts the average for gen AI specialists at $174,727 with top performers clearing $300K. Feels about right to us, maybe even a little conservative for someone with production fine-tuning work on their resume.
I want to spend a minute on MLOps because it’s the most overlooked specialty in this entire guide. Nobody writes excited LinkedIn posts about MLOps. It doesn’t get the breathless media coverage that LLMs get. But try to ship a model to production without somebody who knows monitoring, versioning, automated retraining, and CI/CD pipeline management. Go ahead. Try. We had a client last quarter who spent eleven weeks, almost three months, trying to fill a senior MLOps position before they called us. Eleven weeks of interviews and ghosting and candidates taking other offers. That’s how tight this market is for people who keep the models actually running. We wrote a separate deep dive on MLOps and DevOps hiring profiles for production ML if you want to understand what to look for in those candidates.
Salary by City (Yes, Location Still Matters)
Not as much as it used to. But more than some people want to admit.
| City / Market | Avg Base | Avg Total Comp | Notes |
|---|---|---|---|
| San Francisco / Bay Area | $210K – $250K | $270K – $390K+ | Still the ceiling |
| New York City | $195K – $225K | $240K – $340K+ | Fintech and media are driving this |
| Seattle | $185K – $220K | $230K – $330K+ | No state income tax is a real thing |
| Los Angeles / San Diego | $160K – $200K | $200K – $280K+ | Growing quickly, especially LA |
| Austin | $155K – $195K | $190K – $260K+ | Probably best value in the country right now |
| Boston | $160K – $205K | $210K – $290K+ | Healthcare AI and biotech hub |
| Denver / Boulder | $150K – $190K | $185K – $250K+ | Defense and aerospace AI is the story here |
| Remote (U.S.) | $155K – $210K | $195K – $280K+ | Mostly anchored to national median now |
Sources: Built In (2026), Glassdoor city data (early 2026), MRJ Recruitment zone benchmarks (2026).
Glassdoor has the average AI engineer salary in San Francisco at $212,859. Seventy-fifth percentile is $272,195. Built In actually goes higher at $246,250 average base. Then you add RSUs from Apple or Meta or whoever and senior total comp gets into the $390K neighborhood. Those are real numbers but they represent one very specific slice of the market and if you’re a mid-market company in Denver benchmarking against San Francisco Big Tech, you’re going to make yourself crazy for no reason.
The remote pay story is the more interesting one honestly. In 2022 and 2023 a lot of companies tried discounting remote offers by 15 or 20 percent depending on where the engineer lived. Remember those geographic pay bands? Most of that is gone now. MRJ Recruitment’s 2026 data shows the median senior remote AI engineer salary sitting at about $206,600 and companies have more or less accepted that if you’re hiring remote you’re competing nationally. An AI engineer in Boise who’s great at what they do has the same LinkedIn inbox as the one in San Francisco. The talent pool doesn’t care about your cost-of-living adjustment.
For employers reading this from Austin or Denver or Raleigh, you can still save about 15 to 20 percent versus Bay Area rates for similar quality candidates. That adds up fast when you’re building a team of five or eight people. But it’s not the 30 to 40 percent discount some companies were hoping for.
This Isn’t a Bubble. Here’s Why the Pay Keeps Climbing.
I hear the bubble question at least once a month from clients. When are AI salaries going to come back down to earth? Honest answer? Probably not soon. And I’m not saying that because I’m a recruiter who benefits from high salaries. I’m saying it because the underlying data is pretty overwhelming.
PwC published their 2025 Global AI Jobs Barometer last June. They looked at close to a billion job ads across six continents. What they found was that workers with AI skills earn a 56% wage premium over people in the same roles without AI skills. That number was 25% just one year earlier. It more than doubled in twelve months.
Billion. Job ads. Six continents. This isn’t a survey of 200 people on Reddit.
- Veritone’s Q1 2025 analysis counted 35,445 AI positions across the U.S. with a 25.2% increase from Q1 2024 and a median salary of $156,998 for those roles
- Stanford’s 2025 AI Index Report showed the share of U.S. job postings requiring AI skills climbed to 1.8%, up from 1.4% in 2023, which doesn’t sound like a lot until you remember we’re talking about every job posting in the entire country across every industry
- BLS projects computer and IT occupations will grow much faster than average through 2034 with something like 317,700 openings projected each year, and five of the fifteen fastest growing occupations nationally are in the computer and math category
- Industries most exposed to AI saw productivity growth nearly quadruple since 2022 according to PwC. From 7% to 27%. That’s the real reason companies pay what they pay. These engineers make them more money per employee than almost anyone else on payroll.
So no. Not a bubble. Structural demand across healthcare, finance, manufacturing, and tech. The companies paying $200K+ for AI engineers aren’t being reckless. They’re doing the math on ROI and the math is working.
Industry Breakdown
Tech and finance pay the most. You knew that already. The surprise is healthcare.
Glassdoor’s 2026 industry numbers show media and communications leading at $190,921 median total pay, followed by IT at $167,322, management and consulting at $156,929, healthcare at about $147K, and manufacturing at $139,960. But medians hide the extremes and the extremes in finance are genuinely wild. Senior AI engineers at hedge funds working on trading models or risk systems can see total compensation that rivals Big Tech. We’re talking $400K+ when you factor in bonuses that sometimes double the base. Not most people. But a real part of the market and those firms are pulling from the same candidate pool everyone else uses.
Healthcare is the sleeper. Three years ago most hospital systems didn’t have a single machine learning engineer. Now they’re building entire AI teams and learning the hard way that you have to pay market rates or your job posting just sits there collecting dust. We’re seeing healthcare AI salaries come in about 10 to 15 percent below equivalent tech roles which is much tighter than the 30%+ gap that existed in 2023. Part of the reason is that healthcare AI work is complicated by regulation, HIPAA, FDA considerations, which means you need experienced people not generalists who can sort of figure it out as they go.
Skills That Get You Paid More
LLM fine-tuning is the big one. If you can take a foundation model and customize it for a specific business use case using LoRA, QLoRA, instruction tuning, RLHF, whatever the technique, you are in the highest demand bracket of applied AI roles right now. Analytics Vidhya research cited by Second Talent puts generative AI specialists at an average of $174,727 annually. Top performers break $300K. That tracks with what we see.
RAG architecture is probably the second hottest skill and it went from obscure to essential in about eighteen months. Every company that wants their LLM to know things about their own business, their own data, their own customers, needs somebody who can build retrieval-augmented generation pipelines. Harder than it looks. Much harder. The people who’ve done it well, in production, at scale, have their pick of opportunities.
MLOps and model deployment. I already went on about this. But the short version is that knowing how to get a model into production and keep it there, monitoring performance, managing versions, automating retraining, that stuff adds $15K to $30K to a base salary versus someone who can only build models in notebooks but has never shipped one.
Python shows up in essentially 100% of AI engineering job postings. It’s table stakes. But Python by itself doesn’t move the needle on pay. Python plus serious depth in PyTorch or TensorFlow or JAX plus cloud deployment experience on AWS or Azure or GCP? That’s the combination that separates the $150K engineers from the $200K+ engineers.
Over 75% of AI job listings now seek domain experts with focused deep knowledge in one area rather than generalists. That stat alone should tell you where this market is headed. Pick a lane. The specialists are the ones getting paid.
What You Should Actually Budget If You’re Hiring
I’m going to be direct because this is where most companies get it wrong and then spend months wondering why they can’t close anyone. If you want the full playbook on how to hire AI engineers in 2026, we wrote a whole guide on that too. But here’s the budget reality.
Mid-level AI engineer. Three to five years of experience. Plan on $160,000 to $210,000 in base salary. Then add 15 to 25 percent on top for bonuses, equity, and benefits. Your real loaded cost per year is somewhere between $185,000 and $265,000. If you’re also weighing whether to use a staffing partner, our breakdown of IT staffing agency pricing and fees explains how those costs layer in. Need LLM or generative AI experience specifically? Add another 10 to 15 percent.
Senior AI engineer. Six plus years. Base of $220,000 to $300,000 or more. Total comp between $280,000 and $400,000+. If that makes your finance team uncomfortable, I understand. But posting a senior AI role at $180K base and expecting strong applicants is like listing a house for half the market price and being confused when nobody takes you seriously. The candidates know what their peers are earning. They have Levels.fyi. They have Blind. They talk to each other.
Competing with Google on a smaller budget
You don’t have to match FAANG dollar for dollar. Most companies can’t and shouldn’t try. But you do have to be close enough that the conversation doesn’t end before it starts.
- Move fast. The best AI candidates vanish in two to three weeks. If your process has four interview rounds spread over six weeks you’ve already lost. We watched a client lose their top pick last month because round three got rescheduled twice. Twice. Candidate took another offer before round three even happened.
- Sell the problem, not the perks. AI engineers who’ve spent two years building boring internal tools are desperate to work on something that matters technically. A genuinely interesting problem is worth $20K to $30K in salary for the right person. They won’t always say that explicitly but we hear it in every candidate conversation.
- Remote or hybrid flexibility is just expected at this point. Requiring five days in office for an AI role eliminates maybe 60% of the candidate pool. Possibly more.
- Startup equity can bridge a base gap of $30K to $50K if, and this is a big if, the candidate believes your company has a real trajectory. Engineers have gotten burned too many times on worthless equity to take it on faith anymore. They’ll ask detailed questions about your cap table and your runway and your revenue numbers. Be ready for that conversation or don’t lean on equity as a selling point.
And if you’re stuck or just need to move faster, working with a staffing partner who specializes in this space genuinely helps. Not because we’re magical, but because we talk to AI engineers all day every day and we know what it takes to get them to say yes. Our AI and ML staffing team can usually get qualified candidates in front of you within a week or two.
AI Engineer vs. Data Scientist vs. ML Engineer
The titles are confusing. Even recruiters mix them up sometimes and we literally do this for a living.
| Role | Avg Base (U.S.) | What They Actually Do |
|---|---|---|
| AI Engineer | $140K – $185K | Deploy and integrate AI models into real products, increasingly LLM-focused |
| ML Engineer | $150K – $186K | Build, train, and optimize ML models, more model-building less product work |
| Data Scientist | $120K – $165K | Analysis, experimentation, statistical modeling, more research than engineering |
| Data Engineer | $125K – $170K | Pipelines and infrastructure that make AI work possible |
| Software Engineer | $110K – $155K | General application dev, AI is a specialization layer on top |
Sources: Indeed (Mar 2026), Glassdoor (Feb 2026), Built In (2026).
If you’re hiring across multiple technical roles, our cloud engineer salary guide covers similar benchmarks for that adjacent skill set.
The biggest shift we’ve seen in the last year is that AI engineer roles now focus way more on deploying and integrating pretrained models, especially LLMs, rather than training models from scratch. ML engineers still do more of the traditional model-building and optimization. But half the job postings we see use the titles interchangeably so don’t get too hung up on naming. Write the job description around what the person will actually do day to day and let the title sort itself out. And if you’re specifically looking for data scientists or data engineers rather than AI engineers, our data science and data engineering staffing team handles those placements separately.
Frequently Asked Questions About AI Engineer Salaries
What is the average AI engineer salary in 2026?
Depends entirely on the source. Glassdoor says $140,678. Built In reports $184,757 base with about $211,243 in total comp. Levels.fyi from 9,500+ profiles says $211K median but that skews toward Big Tech. For someone doing real AI/ML work at a typical mid-to-large company, $155K to $200K base is the honest range. Add bonuses and equity on top.
How much do entry-level AI engineers make?
$90,000 to $135,000 in base. Higher in SF and NYC where entry-level starts above $115K. Total comp including signing bonuses and initial equity gets into the $110K to $160K range. Worth noting that “entry level” in AI usually means someone with a CS degree plus master’s coursework or significant project experience, not someone who watched a YouTube tutorial last weekend.
What about senior AI engineers?
$180K to $280K base. Total comp at major tech companies clears $300K and can hit $400K or more. Staff and principal roles at the top companies can exceed $500K in total compensation but that’s the top of the market, not the average.
Do AI engineers make more than regular software engineers?
Yes. A lot more on average. PwC’s 2025 AI Jobs Barometer found a 56% wage premium for roles that require AI skills versus the same roles without. Fifty-six percent. That was 25% just one year earlier.
Which AI specialization pays the most?
Research scientists at places like DeepMind or OpenAI are in their own universe. Total comp can exceed $489K+. But those jobs require a PhD and publications and are a tiny fraction of the market. For applied engineering roles, LLM and generative AI specialists are the highest paid right now with senior roles hitting $240K to $350K+ in base.
Is AI engineering a good career?
Every single metric says yes. Job postings up 25% year over year. Wage premiums doubling annually. BLS projecting much faster than average growth through 2034. Five of the fifteen fastest growing occupations in the entire economy are in the computer and math category. If you have the technical chops to get into this field, the financial trajectory is about as good as anything in professional employment right now.
How much does location affect pay?
SF and NYC pay 20 to 50 percent more than the national average. Seattle is close behind with no state income tax as a kicker. Austin, Denver, and Raleigh sit about 15 to 20 percent below top-tier markets but cost of living differences often make the net outcome better. Remote roles have mostly settled at a national median and don’t get discounted the way they were in 2022 and 2023.
Should we bring in a staffing agency?
If your recruiting team doesn’t have deep experience in AI hiring, or you’ve had a req open for more than three weeks without strong applicants, probably yes. A specialized AI/ML staffing agency like KORE1 has a network of pre-vetted engineers and can usually get people in front of you within a week or two. For senior and specialist roles that speed advantage is often the difference between landing your top choice and watching them take a competing offer. If you want to talk through what the market looks like for a specific role you’re trying to fill, just reach out and we’ll shoot straight with you. You can also try our AI-powered salary benchmark tool for a quick snapshot on any role and market.