Stripe is looking to fundamentally transform how it uses ML, leveraging its extensive and rich dataset to solve some of the most challenging problems in payments and fraud, and to increase the GDP of the internet.
Requirements
- 10+ years of experience in machine learning, with a strong emphasis on modern technologies such as DNNs, Transformers, and Foundation Models.
- A strong builder mentality, with the ability to define a team's charter and lead the development of complex systems from scratch.
- Proven ability to shepherd large, complex ML projects and drive transformational change in an organization.
- Deep passion for solving really interesting problems and for building the latest technologies rather than relying on outdated methods.
- A PhD or Master's degree with a research-oriented background, with the ability to dive into papers and stay current with academic publications.
- Experience with a large-scale, data-rich product in a domain such as payments, commerce, search, or social media.
- Knowledge of the challenges and opportunities in applying ML to fraud prevention, merchant intelligence, or financial services.
Responsibilities
- Lead the development of foundation models for payments, merchants, and other core product areas.
- Develop universal AI Assistants and Retrieval-Augmented Generation (RAG) and LLM systems to automate support and answer questions across Stripe's platforms.
- Drive the adoption of new ML and AI systems to automate merchant onboarding and workflow automation for a majority of our merchants.
- Build and lead a world-class team of ML engineers (MLEs), fostering a culture of technical excellence, collaboration, and continuous learning.
Other
- Proven experience in managing and developing a team of managers, fostering their growth and ensuring alignment with strategic objectives
- In this role, you will be a transformative leader with the responsibility of overseeing three critical teams within ML Foundations.
- You will drive the strategic direction and execution for Stripe's AI initiative shepherding large, complex ML projects from the ground up while upleveling the way the entire company thinks about and utilizes ML.
