AI Jobs in Fintech & Payments

Fintech and payments companies sit at the frontier of applied machine learning in financial services. Unlike traditional banks where AI adoption is layered onto legacy systems, companies like Stripe, Plaid, Robinhood, Affirm, and Ramp were built as software-first businesses — meaning ML is embedded in core product decisions from day one. Stripe's Radar fraud detection system processes billions of data points across its global payment network to block fraudulent transactions in real time. Plaid uses ML to normalize and categorize financial data across thousands of institutions. Affirm's underwriting models make instant credit decisions at checkout using non-traditional data signals.

On the payments infrastructure side, Visa and Mastercard operate some of the most sophisticated ML systems in the world. Visa's AI processes over 200 billion transactions annually, running real-time fraud scoring models that must balance millisecond-level latency with high detection accuracy. Block (formerly Square) applies ML across its seller ecosystem and Cash App for risk scoring, lending decisions, and personalized financial recommendations. Adyen and Checkout.com build ML-powered payment optimization engines that route transactions through the best-performing networks.

For ML engineers and data scientists, fintech and payments offer a rare combination: massive data scale, real-time inference requirements, direct product impact, and a fast-moving engineering culture. Many of these companies also offer competitive equity packages and flexible remote policies, making them attractive destinations for technical talent coming from big tech or academic research who want their models to ship to millions of users within weeks, not quarters.

Who This Is For

  • ✓ML engineers looking to build real-time fraud detection and risk scoring systems
  • ✓Software engineers interested in applying ML to payments infrastructure at scale
  • ✓Product managers with technical depth who want to ship AI-powered financial products
  • ✓Data scientists seeking fast-paced environments where models directly impact revenue and user experience

Open AI Roles in Fintech & Payments

10 roles
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Senior Data Scientist — Data Foundations & AI

Plaid

Build ML models powering Plaid's financial data infrastructure — fraud detection, transaction classification, and data enrichment at billion-transaction scale.

Best for experienced data scientists wanting to work on financial data classification and fraud detection at massive scale.

Data ScienceFintechHybrid
New York, NY / San Francisco, CA$174K – $260K
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New This WeekFeatured

Machine Learning Engineer, Payments ML Accelerator

Stripe

Build deep learning models for fraud detection and authorization optimization at Stripe, processing billions of transactions across the global payments network.

Best for experienced ML engineers with deep learning expertise wanting to work on fraud and payments at global scale.

ML EngineeringPaymentsHybrid
New York, NY / San Francisco, CA / Seattle, WA$212K – $318K
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New This Week

PhD Machine Learning Engineer, New Grad

Stripe

New grad PhD role at Stripe developing foundation models for fraud detection and user behavior prediction across global payments infrastructure.

Ideal for graduating PhD students wanting to apply foundation model research to payments and financial infrastructure.

ML EngineeringPaymentsHybrid
San Francisco, CA / New York, NY / Seattle, WA$180K – $270K
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New This Week

Software Engineer, Machine Learning Infrastructure

Stripe

Build the ML platform powering Stripe's payments infrastructure — data pipelines, model serving, and ML lifecycle tooling at massive scale.

Good entry point for software engineers with ML infrastructure interest wanting to work in fintech and payments.

ML EngineeringPaymentsHybrid
New York, NY / San Francisco, CA / Seattle, WA$157K – $286K
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New This WeekFeatured

Data Scientist, All Levels

Ramp

Tackle high-stakes data problems at Ramp across $100B+ in annualized corporate spend — open to all experience levels with remote option.

Great for data scientists at any level wanting to work on high-impact spend analytics at a leading corporate fintech.

Data ScienceFintechRemote
New York, NY$137K – $297K
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New This WeekFeatured

Machine Learning Engineer I, Underwriting

Affirm

Entry-level ML engineer role at Affirm building credit decisioning models — great opportunity for career changers interested in fintech ML, fully remote.

Ideal entry point for new graduates or career changers wanting to apply ML to credit risk at a leading BNPL company.

ML EngineeringFintechRemote
Remote, US$130K – $170K
View all 10 roles →

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Frequently Asked Questions

What AI roles exist in fintech and payments?
Fintech and payments companies hire ML engineers for fraud detection, transaction risk scoring, and credit decisioning systems. Data scientists build recommendation engines, customer segmentation models, and churn prediction frameworks. NLP engineers work on document verification, chatbot systems, and financial data extraction. Product managers with ML fluency guide AI feature development. Infrastructure engineers build the real-time ML serving platforms that power millisecond-latency predictions. Many fintechs also hire applied research scientists who bridge academic ML research and production systems.
Is fintech a good career path for ML engineers?
Fintech is one of the strongest career paths for ML engineers who want to see their work ship quickly and at scale. Unlike some industries where ML is experimental, fintech companies rely on ML for core revenue — fraud detection at Stripe, credit scoring at Affirm, payment optimization at Adyen. This means ML engineers work on high-priority projects with clear business impact metrics. The engineering cultures tend to be modern (Python, PyTorch, Kubernetes, real-time streaming), and many fintechs offer competitive compensation with meaningful equity upside. The pace is fast, and the feedback loops between model deployment and business results are tight.
What is the culture like at fintech and payments companies?
Fintech culture generally sits between big tech and startups. Companies like Stripe, Plaid, and Ramp emphasize engineering rigor, clear writing, and ownership — you are expected to own problems end-to-end rather than hand off between siloed teams. Payments giants like Visa and Mastercard have more structured environments but are investing heavily in modernizing their engineering cultures to attract top ML talent. Most fintechs offer strong remote or hybrid policies, competitive equity compensation, and faster promotion cycles than traditional financial institutions. The tradeoff is higher pace and ambiguity — you may need to define the problem as much as solve it.

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