Staff ML Engineer, Conversion
US / Canada
Stripe
Stripe powers online and in-person payment processing and financial solutions for businesses of all sizes. Accept payments, send payouts, and automate financial processes with a suite of APIs and no-code tools.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
Stripe Payments Conversion brings together machine learning with product development to increase Stripe’s authentication and authorization success rates at scale, while maintaining competitive transactional cost and minimizing fraud losses for our merchants. Getting this tradeoff right is critical to Stripe’s long term success and profitability. We maximize our merchant’s financial success while minimizing their losses and Stripe’s risk exposure.
The Payments Conversion team consists of machine learning, backend, and full stack engineers who want to tackle this problem through creative new product ideas and impactful machine learning models. We are undertaking several new efforts, where you can have an outsized impact on the architecture, implementation, and design choices behind these systems.
What you’ll do
You’ll be responsible for driving one of Stripe’s most critical objectives – Shaping how Stripe differentiates itself as the best option for maximizing payments conversion efficiently and at scale in a competitive payment service provider ecosystem. You’ll be leading efforts that span multiple teams including Payments, Risk and Fraud to achieve this objective. You’ll also work with external partners, including networks and issuers, and our largest merchants.
Responsibilities
- This effort will include defining the strategy inclusive of deep learning modeling, predictive data, automation, and user experience that arms our merchant with actional performance information.
- Define strategy to enable Stripe and our user’s to understand, detect, and intervene to protect their businesses, and Stripe, from fraud losses while maximizing authentication and authorization success.
- Design and deploy new models to iteratively improve Stripe’s business-critical models and systems that understand a user’s conversion performance.
- Own and drive cross functional partnerships with organizations including GTM, Financial Partnerships, Product, etc., and partner closely with the leadership/stakeholders of various teams within those organizations
- As a leader within Engineering, assist with team growth and development while maintaining a high bar for excellence and technical curiosity.
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
- 15+ years of engineering experience OR equivalent combined work experience reflecting domain expertise as relevant to this position
- Demonstrated experience of leading company-wide initiatives spanning multiple teams and organizations OR leveraging deep domain expertise to influence tech roadmap planning and execution
- Demonstrated ability to effectively collaborate across multiple teams and stakeholders to drive business outcomes
- Demonstrated ability to balance execution and velocity with security, reliability, and efficiency
- Experience, mentoring, and investing in the development engineers and peers
Preferred qualifications
- 15+ YoE as an ML Engineer with minimum of 7+ years of SWE experience
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Architecture Deep Learning Engineering Machine Learning ML models Security
Perks/benefits: Career development Startup environment
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