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.
The Radar ML team builds the machine learning systems that power Stripe’s Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce - helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe.
Key Responsibilities & Day-to-Day Scope
- ✓ Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network.
- ✓ Investigate emerging fraud and abuse patterns, identify where ML can help, and rapidly develop effective solutions.
- ✓ Design features, datasets, evaluation methods, and experiments for noisy, delayed, and adversarial environments.
- ✓ Improve model architectures, training methods, and production systems to increase model quality, coverage, and adaptability.
- ✓ Monitor production behavior and improve models as data, customer needs, and attack patterns change.
- ✓ Balance model quality with legitimate-user impact, latency, reliability, and operational complexity.
- ✓ Partner with engineering, product, data science, risk experts, and leading Stripe users to turn modeling improvements into differentiated Radar products.
- ✓ Contribute to reusable tools and AI-assisted workflows that help the team develop and operate ML systems more effectively.
- ✓ 2+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
- ✓ Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design.
Official Qualifications & Technical Requirements
- • Strong programming and software-engineering skills, with experience writing tested, maintainable code for production ML applications.
- • Experience building or operating production ML systems, including one or more of large-scale data and feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring.
- • Ability to investigate ambiguous problems, reason from data, and choose pragmatic technical approaches.
- • Ability to communicate technical ideas and results clearly and collaborate effectively across disciplines.
- • Experience owning an ML problem across the full lifecycle, from problem framing and data exploration through deployment, monitoring, and iteration.
- • Experience developing or productionizing modern deep learning, graph-learning, or LLM-based approaches.
- • Experience building and optimizing real-time, low-latency ML systems at scale.
- • Experience applying ML to fraud detection, integrity, trust and safety, identity, cybersecurity, or another adversarial domain.
- • Experience rapidly building 0→1 products, working directly with customers, or operating effectively in a fast-moving environment.
- • Active interest in the latest ML developments, and how they can be leveraged to solve business problems.
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.