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2 months ago
Who we areAbout 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.
Find out more about this role by reading the information below, then apply to be considered.
About the team Machine learning is an integral part of almost every service at Stripe. It is a key investment area with products and use cases that span merchant and transaction risk, payments optimization, identity, and merchant data analytics and insights (Sigma). We are also using the latest generative AI technologies (such as LLMs and FMs) to re-imagine product experiences and developing AI Assistants both for our customers (e.g. Radar Assistant and Sigma Assistant), and also to make Stripes more productive across Support, Marketing, Sales, and Engineering roles within the company.
From a data perspective, Stripe handles over $1T in payments volume per year, which is roughly 1% of the world’s GDP. We process petabytes of financial data using our ML platform to build features, train models, and deploy them to production. We use a combination of highly scalable and explainable models such as linear/logistic regression and random forests, along with the latest deep neural networks from transformers to LLMs. Some of our latest innovations have been around figuring out how best to bring transformers and LLMs to improve existing models and also enable entirely new product ideas that are only made possible by GenAI.
What you’ll do As part of the ML Foundations organization you will play a critical role in the acceleration of our Machine Learning journey at Stripe. As a technical leader you will create and drive the long term technical vision for ML Infrastructure. The Machine Learning Infrastructure organization provides infrastructure and support to run machine learning workflows and ship to production, tooling and operational capacity to accelerate the use of these workflows, and opinionated technical guidance to guide our users onto successful paths. You’ll work with engineering leadership and a large cross functional team including engineering, data scientists, product managers and platform infrastructure teams to build the powerful, flexible, and user-friendly systems that substantially increase ML-Ops velocity across the company.
Responsibilities
Create long term technical vision for the org, and identify paths to deliver value in shorter term phases
Lead the 0-1 delivery of powerful, flexible, and user-friendly infrastructure that powers all of ML at Stripe
Designing and building fast, reliable services for ML feature engineering, model training and model serving, and scaling that infrastructure across multiple regions
As a leader within Engineering, assist with team growth and development while maintaining a high bar for excellence and and technical curiosity
Create services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems
Own and build cross-functional partnerships with stakeholders including dependency engineering teams, product, design, infrastructure, and operations
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
Minimum of 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
Experience optimizing the end-to-end performance of distributed systems
Experience designing and implementing data processing systems using the lambda architecture
Experience debugging and optimizing large scale data pipelines using Apache Spark
Experience training and shipping machine learning models to production to solve critical business problems
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