Role Description & Responsibilities
Machine Learning
About Stripe
Stripe is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Machine Learning role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
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.
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.
Key Responsibilities & Day-to-Day Scope
- ✓ 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.
- ✓ 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.
Official Qualifications & Technical Requirements
- • 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.
- • 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.
Preferred Skills & Technology Stack
- + Familiarity with MLOps pipelines (Docker, Kubernetes, Kubeflow, Triton Inference Server).
- + Knowledge of distributed training techniques and GPU acceleration (CUDA).
- + Relevant coursework, research publication, or open-source contribution in ML/DL.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
★ Life & Culture at Stripe
Verified EmployerStripe champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.