ML engineer (banking) Salary in New York (2026): Complete Guide

In New York, an ML engineer in banking typically earns $135,000 to $260,000 base salary in 2026, with total compensation often landing between $170,000 and $400,000+ once bonus and equity are included. If you’re in a front-office-adjacent or model-risk-heavy role at a top bank, the number moves up fast.

Salary by Experience

A few notes on the numbers:

• •Banks pay for risk reduction, not just model quality. If your work touches fraud, AML, credit risk, pricing, or regulatory models, expect stronger comp.
• •New York carries a finance premium. The city is still one of the biggest banking hubs in the US, so compensation reflects both cost of labor and the density of large banks competing for the same talent.
• •Total comp matters more than base. Bonus structure can swing heavily by firm type:
  • •Large commercial bank: lower base growth, steadier bonus
  • •Investment bank / quant-heavy team: higher upside
  • •Fintech or bank-backed platform team: more variable equity

What Affects Your Salary

• •

  Domain specialization

  • •ML engineers who can work on fraud detection, credit decisioning, anti-money laundering (AML), customer segmentation, or risk modeling usually earn more than generic platform ML engineers.
  • •Banking teams pay a premium for people who understand both modeling and regulatory constraints.
• •

  Model governance and explainability experience

  • •If you’ve shipped models that passed model risk management (MRM) review or you know how to document features, drift monitoring, bias testing, and validation artifacts, your value goes up.
  • •In banking, a model that can’t survive audit is not production-ready.
• •

  Front-office proximity

  • •Roles supporting revenue-generating desks or trading-adjacent workflows generally pay more than back-office automation roles.
  • •Even within the same bank, an ML engineer on fraud ops may earn less than one building low-latency signal pipelines for trading analytics.
• •

  Remote vs onsite

  • •Fully remote roles often pay a bit less than hybrid or onsite New York roles.
  • •Banks still like local candidates because of security controls, stakeholder access, and compliance requirements.
• •

  Institution type

  • •Top-tier investment banks and large global banks usually have stronger cash comp bands.
  • •Regional banks and smaller lenders may offer lower base but better work-life balance or faster scope ownership.

How to Negotiate

• •

  Anchor on total comp, not just base

  • •Ask for the full package: base salary, annual bonus target, sign-on bonus, deferred comp if any, and benefits.
  • •In banking roles, a “good” offer can look weak if the bonus target is low.
• •

  Quantify business impact

  • •Bring numbers tied to fraud loss reduction, approval-rate lift, latency reduction, manual review savings, or model performance gains.
  • •Example: “Reduced false positives by 18% while maintaining recall” is stronger than “improved model accuracy.”
• •

  Use compliance as leverage

  • •If you’ve worked with MRM teams, validators, auditors, or legal/compliance stakeholders without slowing delivery down too much, say it clearly.
  • •Banks pay for engineers who can ship under constraints.
• •

  Compare against New York market bands

  • •For mid-level candidates in strong banking ML roles:
    • •Below $170k base is usually light
    • •Around $180k–$200k base is competitive
    • •Above $210k base usually requires niche expertise or strong prior impact
  • •If they can’t move on base due to band limits, push for sign-on bonus or guaranteed first-year bonus.

Comparable Roles

• •

  Data Scientist (Banking) — $140k–$220k base

  • •Slightly lower than ML engineer if the role is more analysis-heavy than deployment-heavy.
• •

  Applied Scientist — $160k–$240k base

  • •Often similar to ML engineer when the work includes experimentation plus production ownership.
• •

  Quantitative Developer — $180k–$280k base

  • •Usually higher if the role sits close to trading or market-making systems.
• •

  MLOps Engineer — $155k–$230k base

  • •Strong comp if you own deployment pipelines, monitoring infrastructure, and governance tooling.
• •

  Risk Model Developer / Model Validation Engineer — $150k–$235k base

  • •Banking-specific role with solid premiums when regulatory expertise is deep.

If you’re evaluating offers in New York banking right now:

• •Prioritize roles with clear ownership of production models
• •Ask whether the team supports fraud/credit/risk/trading workflows
• •Check whether the bonus target is discretionary or formula-driven
• •Confirm whether salary bands are capped by level or location

For this market in 2026; strong ML engineers with banking experience should treat anything below the mid-range as negotiable unless the role gives you unusually good scope or brand value.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.