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 data pipelines, feature development, model evaluation, deployment, monitoring, and iteration.
• Experience working with large and complex datasets and applying data analysis, statistics, and experimentation fundamentals.
• Demonstrated ability to take an open-ended business problem, determine where machine learning can help, and own the solution through production.
• Strong judgment in selecting practical modeling approaches and evaluating tradeoffs among model performance, system complexity, latency, and business impact.
• Strong collaboration skills and the ability to work across teams and contribute to peers' success.

Preferred qualifications

• Experience applying machine learning to fraud detection, risk modeling, payment authorization, identity, account security, or another adversarial domain.
• Experience building real-time, low-latency machine learning or risk decisioning systems at scale.
• Experience integrating models into production services and designing reliable systems around model outputs.
• Experience with payments, fintech, digital wallets, or money movement.
• Strong software engineering skills and experience designing solutions across the machine learning and product stack.