ML engineer (wealth management) Salary in Austin (2026): Complete Guide
ML engineer (wealth management) salaries in Austin in 2026 typically land between $125,000 and $260,000 base salary, with total compensation often reaching $150,000 to $340,000+ when bonus and equity are included. If you’re in a senior or principal seat at a large wealth platform, asset manager, or fintech-adjacent firm, the top end can move higher.
Salary by Experience
| Experience Level | Typical Base Salary (USD) | Typical Total Compensation (USD) |
|---|---|---|
| Entry (0-2 yrs) | $125,000 - $155,000 | $140,000 - $180,000 |
| Mid (3-5 yrs) | $155,000 - $195,000 | $180,000 - $230,000 |
| Senior (5+ yrs) | $190,000 - $235,000 | $220,000 - $290,000 |
| Principal (8+ yrs) | $230,000 - $260,000+ | $280,000 - $340,000+ |
Austin is not a pure finance hub like New York or Charlotte, but it has a strong mix of fintech, SaaS, cloud infrastructure, and enterprise engineering talent. That means wealth management firms hiring here usually pay a premium to compete with tech employers for ML talent.
What Affects Your Salary
- •
Domain depth in wealth management
- •If you’ve worked on portfolio optimization, advisor recommendation systems, client segmentation, risk scoring, or tax-aware rebalancing, you’ll command more.
- •Generic ML experience is good; regulated financial domain experience is better.
- •
Production ML vs research-heavy profiles
- •Companies in wealth management pay more for engineers who can ship models into production with monitoring, retraining pipelines, and governance.
- •Pure modeling without MLOps usually tops out lower.
- •
Regulatory and data governance experience
- •Experience with model risk management, explainability, audit trails, PII controls, and compliance review matters a lot.
- •In this industry, being able to defend a model matters almost as much as building one.
- •
Company type
- •Large established wealth managers often pay strong base plus bonus.
- •Fintechs and VC-backed startups may offer lower base but higher equity upside; some will pay above market if they need senior ML talent fast.
- •
Remote vs onsite
- •Fully remote roles can widen your options beyond Austin employers.
- •Hybrid or onsite roles sometimes trade slightly lower cash for stability or better bonus structure.
How to Negotiate
- •
Anchor on total compensation, not just base
- •Wealth management firms often split comp across salary and annual bonus.
- •Ask for the target bonus percentage upfront so you can compare offers correctly.
- •
Sell your regulatory edge
- •Don’t pitch yourself as “an ML engineer.”
- •Pitch yourself as someone who can build models that survive compliance review, audit scrutiny, and production monitoring.
- •
Quantify business impact in financial terms
- •Talk about reduced churn in AUM terms, improved advisor conversion rates, lower false positives in fraud/risk workflows, or faster client onboarding.
- •In this space, revenue protection and operational efficiency carry real weight.
- •
Use competing Austin offers strategically
- •Austin employers know they are competing with big tech and high-paying SaaS companies.
- •If you have another offer from a nearby market or remote role with stronger comp, use it to reset expectations.
Comparable Roles
- •
Machine Learning Engineer — Fintech
- •Typical Austin range: $145,000-$250,000 base
- •Usually pays close to wealth management if the product touches payments, lending analytics, or fraud.
- •
Data Scientist — Wealth Management
- •Typical Austin range: $130,000-$210,000 base
- •Often slightly below ML engineer because less production ownership is expected.
- •
Applied Scientist — Financial Services
- •Typical Austin range: $160,000-$245,000 base
- •Strong match if the role includes experimentation and model development with business impact.
- •
MLOps Engineer — Enterprise Finance
- •Typical Austin range: $150,000-$230,000 base
- •Pays well when the firm needs model deployment pipelines plus governance tooling.
- •
Quantitative Developer — Investment/Asset Management
- •Typical Austin range: $170,000-$280,000 base
- •Higher ceiling if the role is tied to trading signals or portfolio construction rather than general analytics.
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.
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