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 The Payment Intelligence ML Engineering (PIME) optimizes each of the billions of dollars of transactions processed by Stripe annually on behalf of our customers, maximizing successful transactions while minimizing payment costs and fraud. We leverage ML to serve real-time predictions as part of Stripe's payment infrastructure and risk controls. We own products like,, and end‑to‑end, operating lightning‑fast world‑scale services and cutting‑edge ML models. What you'll do We are looking for Machine Learning Engineers to own the end‑to‑end lifecycle of applied ML model development and deployment in service of consumer‑facing products like,, and. You will work closely with software engineers, machine learning engineers (MLE), data scientists (DS), and ML platform infrastructure teams to design, build, deploy, and operate Stripe's ML‑powered payment decisioning systems, including improving existing ML models and developing new ML solutions. Responsibilities Design and deploy new models using tools (such as Spark, Presto, XGBoost, TensorFlow, PyTorch) and iteratively improve verification and fraud models to protect millions of users from fraud. Envision and develop new models for fraud detection; i.E., work with large payment datasets to find creative new methods of detecting and deterring fraudulent behavior.



Propose new feature ideas and design real‑time data pipelines to incorporate them into our models. Integrate new signals into ML pipelines, derive new ML features, and build workflows to make this process fast. Integrate new models and behaviors into Stripe's core payment flow. Collaborate and execute projects cross‑functionally with the data science, product management, infrastructure, and risk teams. Ensure engineering outcomes meet or exceed established standards of excellence in code quality, system design, and scalability. Mentor engineers earlier in their technical careers to help them grow. Propose and implement innovative product ideas to reduce costs and combat fraud at Stripe. 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 Over 3 years of industry experience building machine learning applications in large scale distributed systems. 2+ years of experience working within a team responsible for developing, managing, and optimizing ML models or ML infrastructure. Experience designing and training machine learning models to solve critical business problems. Experience performing analysis, including querying data, defining metrics, or slicing and dicing data to model performance and business metrics. Preferred qualifications An advanced degree in a quantitative field (e.G., stats, physics, computer science).



Proven track record of building and deploying machine learning systems that have effectively solved critical business problems. Experience in adversarial domains like Payments, Fraud, Trust, or Safety. Experience working in Python, Java and/or Ruby codebases. Experience in software engineering in a production setting. In‑office expectations Office‑assigned Stripe employees 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, Stripe employees 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. This approach helps strike a balance between bringing people together for in‑person collaboration and learning from each other, while supporting flexibility when possible. Pay and benefits The annual US base salary range for this role is $180,000 - $270,000. For sales roles, the range provided is the roles 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 candidates 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. Extra perks for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends. #J-18808-Ljbffr

📌 Machine Learning Engineer, Payment Intelligence (Toronto) - $180,000 - $270,000 A Year
🏢 Stripe
📍 Toronto