Machine Learning Engineer Salary in New York 2026: $145K-$350K+ (Finance, FAANG & Startups)

# Machine Learning Engineer Salary in New York 2026

Machine learning engineers in New York City earn between $145,000 and $350,000+ per year, with significant variation depending on sector. New York's ML market is unlike any other city: it is the only major tech hub where financial services firms compete directly with Big Tech for the same talent pool - and finance often wins on pure compensation.

This guide breaks down ML engineer salaries in NYC by sector, experience level, and company, including the tax reality that erodes those headline numbers.

ML Engineer Salary Ranges in New York (2026)

Finance sector compensation can exceed these ranges significantly - see breakdown below.

The Two-Tier Market: Finance vs Tech

New York's ML market splits into two distinct ecosystems with very different compensation structures.

Finance ML (Highest Paying)

Quantitative hedge funds and investment banks treat ML engineers as profit-center employees, compensating accordingly:

Two Sigma, Citadel, DE Shaw, Renaissance Technologies

Quant ML roles: $300K-$1M+ total compensation

Cash-heavy (bonuses 50-200% of base) vs equity-heavy at pure tech

Performance-linked upside can significantly exceed tech RSU packages

Extremely selective; expect 8-12 interview rounds

Focus: time-series forecasting, NLP for financial signals, portfolio optimization, market microstructure

JPMorgan Chase, Goldman Sachs, Morgan Stanley

ML/AI roles: $180K-$350K total comp

Slower-moving than hedge funds; more bureaucratic

Large ML teams working on risk models, fraud detection, NLP, credit scoring

More stable WLB than hedge funds; structured career paths

Bloomberg

NLP-heavy ML: $180K-$300K total comp

Focus on financial news, data extraction, market intelligence

More startup-like culture for a finance adjacent company

Big Tech NYC Offices (Competitive but Below Finance Ceiling)

NYC has large engineering offices for Google, Meta, Amazon, Microsoft, Netflix, and Spotify. ML engineers here earn:

Google NYC (Hudson Yards): $220K-$420K total comp. Strong ML infrastructure teams, Google Brain adjacent work, Search, Ads.

Meta NYC: $210K-$400K total comp. AI research, ranking systems, integrity ML.

Amazon NYC: $160K-$320K total comp. Alexa, recommendations, fulfillment optimization.

Netflix: $250K-$450K total comp (mostly cash - Netflix famously pays no equity, just high base salaries).

Startups and Scale-Ups

NYC's startup ecosystem is strongest in fintech, health tech, and adtech - all ML-heavy verticals:

Fintech: Plaid, Stripe NYC, Brex, Navan - $160K-$280K with meaningful equity

Adtech: The Trade Desk, Yext, AppNexus alumni companies - $150K-$250K

Health tech: Flatiron Health (Roche), Tempus, Oscar Health - $140K-$240K

Earlier-stage startups: $120K-$180K cash with 0.1-0.5% equity

NYC Tax Reality

New York City has the highest combined income tax burden of any major US city. At $200K income:

Approximate effective combined rate at $200K.

At $180K (mid-senior ML engineer base), you take home approximately $98K-$105K/year after taxes. Add health insurance, 401K contributions, and the higher NYC cost of living, and the take-home feels substantially less than the headline number suggests.

Compare with Seattle: A $200K Seattle ML salary takes home ~$140K/year due to zero state income tax. For equivalent lifestyle, NYC ML engineers need to earn ~40% more than Seattle counterparts to break even. Remote caveat: If your NYC employer allows full remote work from a no-income-tax state (Texas, Florida, Nevada), you keep the NYC salary without the NYC tax bite - but this arrangement is increasingly rare post-2023 return-to-office mandates.

Finance vs Tech: Which Pays More?

For most ML engineers, finance wins on total compensation - but the gap depends heavily on seniority:

The trade-off: finance ML is often less technically interesting by ML research standards (applied prediction vs research), hours are longer at hedge funds, and the work is secretive - you can't publish papers or speak at NeurIPS. For engineers who care about career brand, research visibility, or open-source work, Big Tech offers things quant funds cannot.

What NYC ML Employers Look For

The most in-demand ML skills in New York's market reflect its finance-heavy character:

1. NLP and LLMs - financial news processing, earnings call analysis, regulatory document extraction 2. Time-series modeling - forecasting, anomaly detection, signal generation 3. MLOps and production ML - not just modeling but deploying, monitoring, and scaling models at high throughput 4. Python + SQL - universal; hedge funds also value C++ for latency-sensitive systems 5. Distributed computing - Spark, Ray, or equivalent for large-scale feature engineering

Pure research ML (novel architectures, academic-style work) is less common in NYC than in SF. New York hires ML engineers who ship, not ML researchers who explore.

ML Engineer vs Data Scientist in NYC

The boundary between ML engineer and data scientist is blurry in New York. Key differences:

ML Engineer - builds and deploys production models; strong software engineering + ML

Data Scientist - more analysis-oriented; SQL, experimentation, business insights; generally earns 10-20% less

Research Scientist - purely research, rare outside Google/Meta NYC; PhD typically required; earns ML engineer rates or above

If you're comparing offers, clarify whether a "Data Scientist" role involves production model deployment - it often does at tech companies but rarely at banks, where data scientists feed into ML engineering pipelines.

Is New York Worth It for ML Engineers?

Yes, with caveats.

NYC is the only city in the world where you can earn quant-fund compensation for ML work without moving to a different industry. The finance premium is real and persistent - hedge funds cannot offshore quant research the way banks can offshore operations.

For Big Tech ML, NYC is slightly below SF on absolute total comp, but the talent density, industry diversity, and lifestyle (for those who like cities) make it competitive.

The unavoidable reality: New York's tax burden is brutal, and housing costs are second only to San Francisco among major US tech cities. Run your net numbers, not gross.

See where your ML experience fits on a New York job posting - or browse machine learning engineer salaries across all cities to see the full data.

Frequently Asked Questions

What is the average machine learning engineer salary in New York City? The median ML engineer salary in New York City is approximately $185,000 in base salary. Total compensation including bonuses and equity ranges from $200,000 for mid-level engineers to $400,000+ for senior engineers, with finance roles (Two Sigma, Citadel) reaching $600K-$1M+ for top performers. Do finance companies pay more than FAANG for ML in NYC? For senior and staff ML engineers, yes - top hedge funds pay 2-3× what Big Tech offers. For entry and mid-level engineers, the gap is smaller (20-50%), and Big Tech offers better brand value, more interesting technical problems, and lower pressure environments. How much do Two Sigma or Citadel pay ML engineers in New York? Senior quant ML roles at Two Sigma, Citadel, and DE Shaw typically pay $400,000-$800,000+ in total compensation (base + large cash bonus). Entry-level quant research roles start around $200,000-$300,000. The selection process is extremely competitive with acceptance rates under 1%. What is the NYC income tax rate for ML engineers? New York City ML engineers face a combined marginal rate of approximately 43-47% at $180K-$250K income levels (federal + NY state + NYC city tax + FICA). At $180K income, take-home is roughly $98K-$105K/year before accounting for benefits deductions. Is it better to take a NYC ML job or a remote job based elsewhere? A fully remote job paying NYC rates but based in a no-income-tax state (Texas, Nevada, Florida) is the financial optimal choice - you keep the premium without the tax. In practice, most high-compensation NYC ML jobs (especially finance) require in-office presence, making true remote optimization difficult.