Machine Learning Engineer, Link

Stripe; New York (NY); On-site; USD 212,000 - 318,000; Full time

Benefits offered by this job

Equity

Annual bonus

401(k)

Comprehensive health benefits

Job summary

Stripe is seeking a seasoned machine learning engineer to join the Link Fraud and Auth team. You will build and operate models and risk decisioning systems that protect payments while enabling legitimate transactions at scale.

You’ll work across the ML lifecycle, collaborating with Engineering, Product, and Data Science to move model improvements from idea to production with measurable impact on fraud performance and authorization rates.

Qualifications

• 6+ years building and shipping ML models in production.
• Proficient in Python, SQL, Spark, and XGBoost.
• Experience with data pipelines, model evaluation, and monitoring.

Responsibilities

• Build, deploy, and monitor ML models for fraud and risk.
• Develop real-time risk decisioning systems at scale.
• Collaborate with Engineering, Product, Data Science, and Risk teams.

Skills

Collaboration

Problem solving

Open-ended problems

Tools

Python

SQL

Spark

XGBoost

ML pipelines

Job description

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.

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 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.

In-office expectations

Office-assigned Stripes in most of our locations are currently expected to spend at least 50% of the time in a given month in their local office or with users. This expectation may vary depending on role, team and location. For example, Stripes in Stripe Delivery Center roles in Mexico City, Mexico, Bengaluru, India, and Dublin, Ireland work 100% from the office. Also, some teams have greater in-office attendance requirements, to appropriately support our users and workflows, which the hiring manager will discuss.

Pay and benefits

The annual US base salary range for this role is $212,000 - $318,000. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process.
Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends.

We look forward to hearing from you.

At Stripe, we're looking for people with passion, grit, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Stripe, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us.