ML Engineer Salary in 2026: 7 Cities Compared (With Real Numbers)

Machine learning engineering has become the highest-paying technical specialization in the AI/data space. While data scientists analyze and model, ML engineers build the systems that put those models into production — and companies are paying aggressively for that skill.

The salary range is enormous. An ML engineer in Berlin might earn €60K. The same role in San Francisco can clear $300K. Same title, same core skills, 5x difference in pay.

Here's what ML engineers actually earn in 2026 across 7 major markets.

The Quick Comparison

ML engineers out-earn data scientists by 15–30% in every market on this list. The premium reflects the engineering complexity of deploying and maintaining ML systems at scale.

San Francisco: $156K–$301K

San Francisco is the undisputed capital of ML engineering compensation. The concentration of AI companies — OpenAI, Anthropic, Google DeepMind, Meta FAIR, Scale AI, and hundreds of startups — creates relentless demand for anyone who can train, optimize, and deploy models.

The floor of $156K represents early-career ML engineers at mid-stage startups. The ceiling of $301K is base salary at top AI labs and Big Tech for senior roles. When you factor in equity, sign-on bonuses, and annual bonuses, total compensation for senior ML engineers at companies like Google or Meta regularly exceeds $400K–$500K.

The AI startup scene drives a unique dynamic: pre-revenue companies funded with tens of millions routinely offer $200K+ base to attract talent from Big Tech. Equity at these companies is a bet, but the base alone is substantial.

The cost of living needs no caveat anymore — everyone knows SF is expensive. The relevant question is whether the salary premium outpaces the cost premium. For ML engineers, it usually does.

Full breakdown: ML Engineer Salary in San Francisco

Seattle: $119K–$328K

Seattle has the widest salary range on this list, and for good reason. The city houses both Amazon's massive ML infrastructure teams and a growing cluster of AI startups — each with very different pay structures.

Amazon is the dominant employer. Their ML engineer roles span everything from Alexa's NLP stack to AWS SageMaker to recommendation systems for the retail platform. Amazon's pay structure is notoriously back-loaded with equity (Years 3 and 4 pay significantly more than Years 1 and 2), so the headline base salary understates total comp for tenured employees.

The $328K ceiling reflects senior ML engineers at companies competing directly with Big Tech — often AI-focused startups offering premium base salaries to compensate for riskier equity. Microsoft's ML teams in Redmond (effectively the same metro) also push salaries up.

Seattle's killer advantage: no state income tax. An ML engineer earning $200K in Seattle takes home roughly $20K more per year than the same salary in San Francisco or New York. Over a career, that compounds into hundreds of thousands of dollars.

See the numbers: ML Engineer Salary in Seattle

New York: $140K–$267K

New York's ML engineering market is driven by two forces: finance and Big Tech's east coast offices.

Quantitative trading firms — Citadel, Two Sigma, Jane Street, D.E. Shaw — hire ML engineers for prediction models, signal processing, and automated trading systems. Compensation at these firms is exceptional: base salaries of $200K+ with bonuses that can double or triple total pay. The catch is intensity — these are demanding roles with high turnover.

Big Tech's NYC offices (Google, Meta, Amazon, Apple) pay at parity with their west coast headquarters, making New York competitive with San Francisco on base salary while offering a different lifestyle.

The startup scene is smaller than SF's for pure ML but growing fast, particularly in fintech, health tech, and ad tech. Companies like Runway (AI video generation) and Hugging Face (with a large NYC presence) represent the city's AI startup culture.

New York's combined state and city taxes are high — potentially 12%+ on top of federal — which narrows the gap with lower-tax cities like Seattle. Still, for ML engineers targeting finance, NYC is unmatched.

Full data: ML Engineer Salary in New York

Remote (US): $135K–$215K

Remote ML engineering roles have exploded since 2023. The nature of ML work — iterating on models, running training jobs on cloud infrastructure, writing deployment pipelines — translates well to remote environments. You don't need to be in the office to submit a training run to an A100 cluster.

The salary range ($135K–$215K) is tighter than office-based roles because remote pay often comes with location adjustments. Companies headquartered in SF/NYC that hire remote may discount 10–20% for employees in lower cost-of-living areas.

The arbitrage opportunity is real: a remote ML engineer earning $180K while living in Austin, Denver, or Lisbon has significantly more purchasing power than a $250K engineer commuting in San Francisco.

Two things to watch for in remote offers: 1. Pay band transparency — Does the company publish location-based pay bands, or is it ad hoc? 2. Equity treatment — Some remote-first companies give the same equity regardless of location, making the total comp arbitrage even larger.

Current ranges: ML Engineer Salary — Remote

Toronto: C$110K–C$175K

Toronto has quietly become one of the world's most important ML engineering hubs, largely thanks to the University of Toronto and the Vector Institute. Geoffrey Hinton's deep learning research group launched entire careers (and companies) in the city, creating a self-reinforcing talent ecosystem.

Google DeepMind has a major Toronto office. So do Uber (ATG successor), Samsung AI, LG AI, and dozens of ML-focused startups. The city produces more ML PhDs per capita than almost any North American market.

Salaries of C$110K–C$175K convert to roughly US$80K–US$128K, which looks modest. But Toronto's advantages are real: universal healthcare, relatively affordable housing (compared to SF/NYC), and a straightforward immigration system for skilled workers. Canada's Global Talent Stream can process work permits in weeks.

For international ML engineers, Toronto is one of the easiest high-quality markets to enter. The salary is lower than US cities, but the total life package — healthcare, immigration stability, quality of life — often compensates.

See the breakdown: ML Engineer Salary in Toronto

Berlin: €60K–€130K

Berlin is Europe's scrappy ML hub. The salaries won't compete with the US, but the value proposition is unique.

An ML engineer in Berlin earning €95K takes home roughly €4,800/month after taxes and healthcare. Rent for a good one-bedroom: ~€1,100. That leaves substantial disposable income — comparable to a $170K salary in San Francisco after equivalent expenses.

Berlin's ML scene is anchored by companies like Zalando (recommendation systems), DeliveryHero (logistics ML), Ada Health (medical AI), and a growing cluster of AI startups funded by European VCs. The research side is strong too: TU Berlin and Fraunhofer institutes produce ML talent that stays local.

The €60K–€130K range reflects the gap between early-career roles at bootstrapped startups (bottom) and senior positions at well-funded scale-ups or Big Tech satellites (top). Google's Berlin office, for instance, pays at the top end of this range or above it.

What Berlin offers that no US city can match: 30 days annual leave (standard), strong labor protections, excellent public transit, and a work culture that actually respects boundaries. If you've burned out grinding 60-hour weeks at a Bay Area startup, Berlin's pace is restorative.

The EU Blue Card path makes Berlin accessible to non-EU candidates. For an ML engineer salary above €58K (easily achievable), you qualify automatically.

London: £60K–£157K

London is Europe's highest-paying market for ML engineers, edging out Berlin and competing with Toronto on absolute numbers.

Google DeepMind's headquarters in London is the city's crown jewel for ML talent. Meta AI Research, Amazon Science, and Microsoft Research also maintain significant London teams. Compensation at these labs matches or exceeds most European benchmarks — senior researchers and ML engineers at DeepMind can earn £120K–£157K base, with total comp going higher.

The finance sector creates additional demand. Quantitative hedge funds like Man Group, GSA Capital, and Marshall Wace hire ML engineers for trading systems, and pay structures mirror (at smaller scale) their New York counterparts.

London's salary range is wide because the city has both world-class AI labs paying top dollar and a long tail of startups and mid-size companies at £60K–£80K. The bimodal distribution means "average" salary statistics are misleading — you're either in the premium tier (Big Tech / finance) or the standard tier (everyone else).

After adjusting for benefits — NHS healthcare, 25+ days leave, employer pension contributions — London's effective compensation narrows the gap with lower-benefit US roles.

Current data: ML Engineer Salary in London

What Drives ML Engineer Salaries in 2026

The LLM Premium

The single biggest salary driver in 2026 is LLM expertise. ML engineers who can fine-tune large language models, build RAG systems, optimize inference for production, or train custom models command a 20–40% premium over general ML engineers.

This premium exists because: 1. The technology moves faster than the talent pool can grow 2. Mistakes in LLM deployment are costly (hallucination, data leakage, compliance failures) 3. Companies across every industry are racing to ship AI products

If you're an ML engineer debating where to specialize, LLM systems engineering is the most lucrative bet in 2026.

MLOps and Infrastructure Pay Is Catching Up

The "glamorous" side of ML engineering — training models, doing research — historically paid more than the "plumbing" side (deployment, monitoring, CI/CD for ML). That gap is closing fast.

Companies have realized that getting a model into production reliably is often harder than building it. ML engineers who specialize in:

Model serving and inference optimization

Training pipeline automation

Feature stores and data pipeline engineering

ML monitoring and observability

...are increasingly valued at parity with model-focused ML engineers. At some companies, MLOps-focused engineers earn more because they're rarer.

Company Stage Matters

The tradeoff between Big Tech (stable, liquid equity, high base) and startups (potentially life-changing equity, lower base) is one every ML engineer navigates. There's no universally right answer — it depends on your risk tolerance, career stage, and financial situation.

Experience Curve

ML engineering has one of the steepest experience-salary curves in tech:

The jump from junior to mid-level is where ML engineers see the largest percentage increase. This transition typically requires going from "can implement papers" to "can design and ship production ML systems end-to-end."

How to Maximize Your ML Engineer Salary

1. Build Production ML Systems

Academic ML knowledge gets you in the door. Production experience — deploying models that handle real traffic, building training pipelines that run reliably, debugging model performance in production — is what gets you to the top of the pay range.

If your resume shows only Jupyter notebooks and Kaggle competitions, you're leaving money on the table. Ship something real.

2. Know Your Market Rate

Before any salary conversation, check the actual market rate for your combination of role, city, and experience. Use CareerCheck's ML engineer salary data for a specific breakdown.

A 10-minute salary check before negotiating can be worth $20K+ per year. Most people negotiate blind, and most people are underpaid.

For specific tactics, see our salary negotiation guide.

3. Specialize Where Demand Outpaces Supply

In 2026, the highest-premium ML specializations are:

LLM fine-tuning and deployment — Every company wants this

Real-time ML inference — Low-latency serving at scale

ML security and safety — Growing as AI regulation tightens

Multimodal models — Vision + language + audio systems

Picking the right specialization can be worth more than switching cities.

4. Consider the Tax Arbitrage

An ML engineer earning $200K in Seattle takes home ~$20K more per year than $200K in New York, purely from state tax savings. Over 5 years, that's $100K.

If you're location-flexible, the tax math should be part of your decision. Our city-specific salary pages include context on local tax impact — check the full ML engineer salary explorer to compare.

Check Your Market Value

ML engineering compensation moves fast. New model architectures create new specializations. New companies raise funding and start competing for talent. What was competitive six months ago might be below market today.

Look up your specific combination on CareerCheck's salary explorer — we break down ML engineer salaries across all major markets with real data. Then use the career fit analysis to see how your skills match against current openings.

The ML engineers earning $300K+ aren't just better at math. They understand their market position, negotiate with data, and choose their specialization strategically.