Machine Learning Engineer, Link

Stripe·New York Citysenior

In short

→Ingeniero de ML que construye modelos para prevenir fraude en pagos digitales.
→Trabajas con grandes volúmenes de datos en tiempo real para mejorar la seguridad y aceptación de pagos.
→Destaca el impacto directo en Link's revenue engine: Instant Bank Payments.

Proficiency in English required for collaboration across global teams.

In ~1 minute you get: who interviews you, the likely questions answered from your CV, and your CV tailored to this job. Free, no card.

🎧Land the interview? Bring the copilot. Our free extension listens to the live interview and flashes 3-4-word anchors from your resume and prep — glance, connect, talk. Get the extension →

What they ask for

✓6+ años de experiencia en modelos de ML en producción.
✓Dominio de Python, SQL, Spark y XGBoost.
✓Experiencia con sistemas de ML en producción.
✓Habilidades para diseñar pipelines de datos y características.
✓Capacidad para trabajar en problemas ambiguos desde el análisis hasta el despliegue.
✓Colaboración efectiva con equipos de ingeniería, producto y ciencia de datos.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PythonSQLSparkXGBoosttree-based modelsdata pipelinesfeaturesevaluation methodsexperimentsmonitoring systems

Who should you write to at Stripe?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Link is a digital wallet designed for fast and secure online payments. It allows consumers to save and use their preferred payment methods across the Link network, helping them check out quickly and securely wherever Link is accepted. The Link Fraud and Auth team works to make Link the most trusted and highest-performing way to pay. We protect consumers and merchants from fraud, abuse, and financial loss while maximizing authorization rates for good users. Our work spans consumer-facing experiences, payment infrastructure, and ML powered risk systems. We manage fraud and financial risk across a growing range of novel Link features, including Link’s agentic wallet, stored balance, and LPMs. The team also owns Instant Bank Payments, a proprietary payment method built on ACH rails, offering merchants immediate confirmation while protecting them from bank-initiated returns. IBP is the heart of Link’s revenue engine, giving LFA engineers the opportunity to shape and scale one of Link’s most important products. What you’ll do As a machine learning engineer on Link Fraud and Auth, you’ll build and operate models and risk decisioning systems that protect Link while helping more legitimate payments succeed. You’ll work across the full machine learning lifecycle, from analyzing fraud patterns and identifying opportunities to building, deploying, monitoring, and improving models in production. You’ll use data to form hypotheses, make practical modeling choices, and define technical direction in partnership with Engineering, Product, and Data Science. Your work will directly influence Link’s fraud performance, authorization rates, and ability to expand into new products and payment experiences. Responsibilities • Build, train, evaluate, deploy, and own machine learning models that detect fraud and abuse across Link. • Use large-scale datasets to investigate emerging threats, develop hypotheses, and identify opportunities to improve payment performance. • Develop pragmatic machine learning solutions, including tree-based models and other approaches suited to real-time risk decisioning. • Design data pipelines, features, evaluation methods, experiments, and monitoring systems that support reliable production models. • Build and improve risk decisioning systems that integrate with other parts of Stripe’s payments stack. • Own ambiguous problems from initial analysis and problem definition through technical design, implementation, launch, measurement, and iteration. • Collaborate with Engineering, Product, Data Science, and Risk partners across Stripe to turn model improvements into durable product outcomes. Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements • 6+ years of industry experience building and shipping machine learning models in production. • Strong programming skills in Python and experience with common data and machine learning tools, such as SQL, Spark, and XGBoost. • Strong knowledge of production machine learning systems, including da

Looking for something similar?

Leave your email and we'll alert you when matching jobs appear.

Don't apply unprepared

We research who's interviewing you, tailor your CV and rehearse you live — first one free.