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. Headquartered in San Francisco and Dublin, the company aims to increase the GDP of the internet.

Stripe

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 organization 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 Radar, Adaptive Acceptance, and Identity 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 Radar, Adaptive Acceptance, and Identity. 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 fraudEnvision 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 fastIntegrate new models and behaviors into Stripe’s core payment flowCollaborate and execute projects cross-functionally with the data science, product management, infrastructure, and risk teamsEnsure engineering outcomes meet or exceed established standards of excellence in code quality, system design, and scalabilityMentor engineers earlier in their technical careers to help them growPropose 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 industry experience building machine learning applications in large scale distributed systems.2+ year of experience working within a team responsible for developing, managing, and optimizing ML models or ML infrastructureExperience designing and training machine learning models to solve critical business problemsExperience 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 problemsExperience in adversarial domains like Payments, Fraud, Trust, or SafetyExperience working in Python, Java and / or Ruby codebases Experience in software engineering in a production environment.


Hybrid work at Stripe

This role is available either in an office or a remote location (typically, 35+ miles or 56+ km from a Stripe office).

Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.

A remote location, in most cases, is defined as being 35 miles (56 kilometers) or more from one of our offices. While you would be welcome to come into the office for team/business meetings, on-sites, meet-ups, and events, our expectation is you would regularly work from home rather than a Stripe office. Stripe does not cover the cost of relocating to a remote location. We encourage you to apply for roles that match the location where you currently or plan to live.

Pay and benefits

The annual US base salary range for this role is $173,100 - $316,800. 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.

Remote Machine Learning Engineer Job in United States Stripe

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