AI engineer salary: India vs US vs UK (2026 data)

A mid-level machine learning engineer in the US earns a median total compensation of $260,780 according to Levels.fyi. The same role in India pays roughly ₹24.6 lakh, which works out to about $29,000, though in practice it skews higher in Bangalore. That is a 9x gap for comparable work.

Not a rounding error. A structural difference.

But the number that actually matters when you are making a hiring decision is not salary. It is total employer cost. And when you factor in stock grants, payroll taxes, health insurance, recruiting fees, and the three to six months of lost productivity during a slow US hiring cycle, the real gap between markets gets even wider than the headline salary numbers suggest.

This guide covers what AI engineers actually earn in 2026 across the US, UK, and India, broken down by role, seniority, and city. Six role families: ML engineer, data scientist, MLOps, LLM engineer, computer vision, and NLP. We also explain why salary data sources disagree with each other (sometimes by 50% or more) and what that means for your benchmarking.

What AI engineers earn in 2026 at a glance

Master salary reference: role × market × seniority

All figures are annual. US and UK numbers reflect total compensation (base + stock + bonus). India figures reflect cash compensation (base + bonus); total comp including ESOPs at top employers runs 20–40% higher at senior levels. Sources: Levels.fyi, SalaryExpert, PayScale, AIJobs (2025–2026).

Detailed breakdowns by city (for India) and by source methodology follow below.

United States

The US is still the most expensive market for AI talent by a wide margin. Levels.fyi puts the median total compensation for US machine learning engineers at $260,780. That includes base salary, stock, and bonus. Data scientists sit in a broad range: $130,000 to $244,800 on the same platform, with ML-focused data scientists closer to a $180,000 median.

And those are medians. At Google, Meta, and OpenAI, senior ML engineer total comp regularly exceeds $300,000 and can push past $500,000 at the staff level. H-1B wage data for senior AI and ML roles at some of these employers shows base salaries above $300,000 before stock is even included.

But here is the thing about Levels.fyi data: it has a big-tech bias. The people who report their compensation on Levels.fyi are disproportionately at high-paying companies. Nobody at a 50-person healthcare analytics firm in Ohio is rushing to post their salary on a platform dominated by FAANG engineers. So the $260,780 median is real, but it represents the top of the market, not the whole market.

United Kingdom

UK AI salaries run substantially lower than the US. PayScale puts the average UK machine learning engineer salary at £52,775 and data scientists at £41,644. London pushes higher: £60,788 for ML engineers on PayScale.

Then you look at Levels.fyi for London data scientists and see a range of £64,364 to £111,288. That is a completely different picture from the PayScale numbers. The gap tells you which employers are included in each dataset. PayScale captures a cross-section of UK companies including mid-market firms outside London. Levels.fyi captures Google DeepMind, Meta London, and similar big-tech offices where comp is structured more like the US.

Both are real salary data. They just measure different populations.

India

SalaryExpert's 2026 estimates place entry-level AI engineers at ₹16.1 lakh (roughly $19,000) and senior AI engineers at ₹26.2 lakh (roughly $31,000). Machine learning engineers average ₹24.6 lakh nationally, with entry-level at ₹17.5 lakh and senior at ₹28.6 lakh.

These are broad-market averages. Engineers at top product companies, GCCs, and well-funded startups in Bangalore regularly earn 30 to 50% above these figures.

A mid-stage healthtech company we work with hired a senior ML engineer in Hyderabad at ₹38 lakh. The same profile in Boston would have cost $240,000 in total comp. The engineer had six years of production experience at a major GCC and was shipping models within his second week. That kind of outcome is not the exception anymore. It is what happens when you hire from a market where production ML experience has been accumulating inside GCCs for a decade.

When India salary data is misleading

Before you take any of these numbers at face value, there are real problems with how AI salaries get reported in India.

The biggest one: "AI engineer" means wildly different things at different companies. At one company it means a person building and training custom models from scratch. At another it means someone integrating a third-party API and writing prompt templates. Both get filed under the same job title in salary databases, and the compensation difference between those two jobs is easily 2x.

Inflated titles make it worse. A data analyst doing basic SQL work might carry the title "Data Scientist" because it helps with recruiting and retention. That person's ₹12 lakh salary pulls the market average down in ways that make experienced ML engineers look underpaid relative to reality.

Then there is the gap between advertised salaries and actual offers. Job postings in India frequently list ranges that are 15 to 20% higher than what gets offered, especially at mid-market companies using salary bands to attract candidates who will then be negotiated down.

And GCC salaries distort broad market averages in the other direction. When Google India or Microsoft Hyderabad pays ₹45 to ₹60 lakh for a senior ML engineer, that pulls averages up in a way that does not reflect what a Series B startup or a mid-size IT product company actually pays for similar talent.

The honest answer: use multiple sources, attach a city to every number, and ask what kind of company is paying that salary. Averages alone will mislead you.

Salary methodology: how to read AI compensation data

Base salary vs total compensation

This distinction matters more in AI than in most fields because stock compensation at top US AI employers is enormous. A Google ML engineer making $180,000 base might have $260,000 in total comp once stock and bonus are added. If you are comparing a Levels.fyi number (total comp) against a PayScale number (base-weighted), you are comparing two different measurements and the gap will look artificially wide.

For employer benchmarking, the number that actually matters is total cost to employ: salary plus benefits plus payroll taxes plus recruiting costs plus hiring infrastructure. That is what determines whether hiring in India, the US, or the UK makes financial sense for a given role.

Why sources disagree

Levels.fyi captures total compensation and skews toward big-tech employees who are more likely to report. PayScale and SalaryExpert lean toward base salary or cash compensation and capture a broader set of employers. City concentration matters too: a US "average" that is 60% Bay Area engineers looks nothing like one that includes Austin, Denver, and Raleigh.

AI role titles are still poorly standardized. One company's "ML engineer" is another company's "data scientist" is another company's "AI platform engineer." The same person could appear at different salary points depending on which title the database filed them under. And in India, as covered above, the title problem is even more pronounced.

Why UK data often looks inconsistent

Because PayScale and Hays capture the broad UK market while Levels.fyi disproportionately represents big-tech London offices. A machine learning engineer at a mid-size UK company outside London earns something very different from one at Google DeepMind. When you see PayScale saying £52,775 and Levels.fyi implying £80,000+, neither source is wrong. They are measuring different segments of the same market.

AI engineer salary by role in 2026

Machine learning engineer salary

The most clearly benchmarked AI role across all three markets.

US: Levels.fyi median total comp of $260,780. Base salaries at mid-level typically run $160,000 to $200,000 before stock and bonus.

UK: PayScale average of £52,775 nationally, £60,788 in London. Levels.fyi London numbers run much higher for big-tech roles.

India: SalaryExpert national average of ₹24.6 lakh ($29,000). Bangalore average of ₹27.9 lakh ($33,000). Top GCC and product company roles push to ₹40 to ₹55 lakh at senior levels.

Data scientist salary

The widest salary band of any AI role because "data scientist" covers everything from SQL analysts doing dashboards to people building Bayesian hierarchical models.

US: Levels.fyi range of $130,000 to $244,800. ML-focused data scientists land around a $180,000 median.

UK: PayScale national average of £41,644. London PayScale shows £38,361 (yes, lower than the national figure, probably a sample composition issue). Levels.fyi London range: £64,364 to £111,288, reflecting a very different employer mix.

India: Bangalore data scientist average of ₹26.2 lakh ($31,000) on SalaryExpert. Junior roles start around ₹12 to ₹15 lakh. Senior data scientists with domain expertise and leadership reach ₹50 to ₹65 lakh at top companies.

MLOps engineer salary

MLOps is under-reported in most public salary databases. The title is relatively new and many companies classify these engineers under "platform engineering" or "ML infrastructure." But the market signal is clear: engineers who can build model serving infrastructure, feature stores, and automated retraining pipelines are in short supply everywhere. In the US, expect $180,000 to $280,000 total comp. In India, strong MLOps engineers command ₹25 to ₹50 lakh depending on experience and city, which carries a premium over general software engineering.

LLM engineer salary

Still under-standardized in salary databases. Most public sources have not caught up to the market reality. What the data does show: 2026 job-market snapshots from AIJobs and live listings confirm that LLM-focused roles (RAG systems, fine-tuning, agent architectures) are priced at the top of the AI pay band. In the US, senior LLM engineers at well-funded startups earn $200,000 to $350,000+. In India, the pool is growing fast in Bangalore and Hyderabad, with compensation running ₹20 to ₹45 lakh for mid-to-senior engineers.

Specialist roles: computer vision and NLP

Computer vision and NLP are both specialist titles with thin public salary data. AIJobs archived salary pages show senior computer vision engineers in high-paying bands, particularly in the US where autonomous vehicle, medical imaging, and manufacturing companies compete for a small talent pool. US ranges: $170,000 to $300,000 total comp for mid-to-senior. India ranges: ₹18 to ₹45 lakh, with the upper end reserved for engineers with production deployment experience.

NLP as a standalone title has been partially absorbed into the broader LLM and "language AI" category since 2023. Many engineers who would have been called NLP specialists five years ago now work on LLM applications, and salary databases have not fully caught up to that shift. In practice, NLP-focused compensation tracks ML engineer bands, with premiums for engineers who have production experience with named entity recognition, text classification, or multi-language models at scale. Especially at senior levels.

Any page presenting clean single-number averages for LLM, computer vision, NLP, or MLOps is either pulling from a thin sample or smoothing over real variance. What you can rely on: the relative order (US pays most, UK second, India third) and the general magnitude of the gaps.

Junior, mid, and senior salary bands

Junior

India entry-level benchmarks from SalaryExpert: AI engineers at ₹16.1 lakh ($19,000), ML engineers at ₹17.5 lakh ($21,000). These are broad-market figures. A junior ML engineer at a top Bangalore startup or GCC starts 20 to 40% above these numbers.

US junior AI and ML roles typically start at $120,000 to $150,000 total comp. UK junior roles: £30,000 to £42,000 base.

Mid-level

India national averages sit around ₹20 to ₹30 lakh for AI and ML engineers. US mid-level AI engineers: $170,000 to $260,000 total comp. UK mid-level: £45,000 to £65,000 base, higher in London.

The mid-level band is where the India vs US cost gap hits employers hardest. A mid-level ML engineer costing $220,000 all-in in the US might cost $35,000 to $45,000 through an EOR model in India. That frees up budget for two or three additional engineers at the same total spend. Not a marginal saving. A fundamentally different team-building math.

Senior

India senior AI engineer benchmarks: ₹26.2 lakh ($31,000) on SalaryExpert, but real senior compensation at top companies runs ₹40 to ₹70 lakh ($48,000 to $83,000). India's senior ML engineers sit at ₹28.6 lakh on SalaryExpert, again with significant upside at top employers.

In the US, senior and staff AI roles at top companies exceed $300,000 total comp regularly. H-1B wage data shows AI and ML roles at some employers with base salaries above $300,000 before stock is added. In the UK, senior roles at DeepMind, Meta London, and similar companies can reach £100,000 to £150,000 total comp.

The senior band is also where India's salary data becomes least reliable. At this level, compensation packages increasingly include ESOPs, retention bonuses, and custom structures that do not show up in standard salary surveys. A senior ML engineer at Flipkart or Razorpay earning ₹55 lakh in total comp, actually more like ₹70 to ₹80 lakh when you include vested stock, is invisible in SalaryExpert data.

India salary by city

Bangalore

The highest-paying city in India for AI talent. SalaryExpert shows ML engineers at ₹27.9 lakh and data scientists at ₹26.2 lakh on average, roughly 13 to 14% above national averages. At top-tier Bangalore employers the premium is steeper. Senior ML engineers at product companies and GCCs here command ₹40 to ₹65 lakh, and a small number of candidates push past ₹80 lakh at companies like Google, Microsoft, and well-funded AI startups.

Hyderabad

The fastest-growing AI hub in India. Strong GCC presence from Microsoft, Amazon, Google, and ServiceNow. Salaries run roughly 5 to 15% below Bangalore for equivalent roles. Mid-level ML engineers in Hyderabad typically earn ₹18 to ₹30 lakh, senior roles ₹30 to ₹50 lakh. The cost-quality tradeoff is genuinely attractive: you get engineers from overlapping talent pools at lower compensation pressure and lower attrition.

Pune

A strong mid-cost market. AI salaries here typically run 15 to 25% below Bangalore. Pune's university and IT services ecosystem produces solid data science and product engineering talent. Good for teams where cost efficiency matters and the work is well-defined applied ML or analytics.

Chennai

Stable and underrated. Chennai's enterprise engineering culture means lower attrition and reliable long-term team building. AI salaries are similar to Pune, sometimes slightly lower. Strong for data platform, analytics, and backend AI infrastructure roles.

Delhi NCR and Mumbai

Delhi NCR shows up in top-five AI job-posting data, particularly for fintech AI and enterprise analytics. Mumbai matters for financial services AI. Neither matches Bangalore or Hyderabad for pure AI engineering depth, but both are worth considering depending on your industry vertical.

India vs US vs UK: what employers should actually compare

Salary alone is not total cost

A $170,000 US AI engineer does not cost $170,000 to employ. Add employer payroll taxes (roughly 10%), health insurance ($15,000 to $25,000), 401(k) match, recruiting fees (20 to 25% of first-year salary for AI roles), and the three to six months of productivity lost to a slow hiring process. Real year-one cost: $220,000 to $300,000. UK employer costs sit lower but still include National Insurance, pension contributions, and recruitment overhead that adds 20 to 30% above base salary.

In India through an EOR model, the employer cost is the engineer's salary plus employment infrastructure covering payroll, provident fund, ESIC, gratuity, medical insurance, and local HR support. Total year-one cost for a strong mid-level ML engineer: $35,000 to $50,000 all in. That is roughly one-fifth of the US cost for the same work output.

Total comp is especially distorted in the US

Levels.fyi data makes it clear that stock and bonus can represent 30 to 50% of total compensation at top US AI employers. A $180,000 base becomes $260,000 in total comp once stock vests. This inflates US salary averages compared to markets where equity compensation is less common. When comparing US vs India or UK costs, base salary is a more apples-to-apples comparison than total comp, and total employer cost is the most honest number of all.

Why India is a strategic hiring market

India is not just a cost play. The talent pool is structurally deep: over 65% of India's 1.4 billion people are under 35, the country produces more STEM graduates annually than any other market, and the AI ecosystem across GCCs and startups is training production-grade engineers at scale.

But you do need hiring infrastructure to make it work. Payroll compliance in India involves provident fund, ESIC, gratuity, professional tax, and TDS, none of which you want to manage from overseas. Kaamwork handles all of that at a flat $599/month per employee (kaam.work/pricing), with no entity setup required and engineers onboarded in days rather than months. The salary gap is real, but capturing it without compliance risk is where the operational model matters.

Use the cost calculator before you benchmark

Salary tables are useful but they only get you halfway. Every company has a different role definition, a different city preference, and a different comp philosophy.

The global cost calculator at kaam.work/global-cost-calculator lets you plug in your specific role, seniority level, and target market and see actual employer cost side by side. It will give you a clearer answer in thirty seconds than an hour of cross-referencing salary databases that each measure something different.

Use these salary benchmarks to hire smarter in 2026

There is no single "AI engineer salary." Country, city, role, seniority, and company type each shift the number. The US pays the most by a wide margin, driven by stock compensation at top employers. The UK sits in the middle, with broad-market salaries well below US levels but a meaningful premium at big-tech London offices. India is the lowest-cost market and, increasingly, the deepest talent market for applied ML, data science, and GenAI engineering.

When you are making hiring decisions, compare total employer cost rather than headline salary. A $170,000 US ML engineer costs $250,000+ once you add benefits, taxes, and recruiting overhead. A comparable engineer in India costs $35,000 to $50,000 all in. That difference is large enough to change your entire team-building strategy.

And if the math works for your team, Kaamwork can get your first India AI hire onboarded and working within two weeks, with full compliance, payroll, and benefits handled from day one (kaam.work/talk-to-us).

The numbers are public. The talent is there. Run the math and see where it takes you.

Disclaimer: Salary data in this guide is based on publicly available 2025-2026 estimates from Levels.fyi, SalaryExpert, PayScale, AIJobs, and industry commentary. Actual compensation varies by candidate, company, city, role definition, and compensation structure. Kaamwork pricing is current as of April 2026.