Stripe’s mission is to build the economic infrastructure for the internet. Supportability Intelligence builds models and systems to understand Stripe’s users at scale. Our systems enforce Stripe’s Terms Of Service; classify merchants into industry groupings; and detect extremist groups in the Stripe ecosystem. We’re critical for ensuring that Stripe understands who’s using our products and for what; that we’re a responsible force for good in the Fintech ecosystem; and that the rest of Stripe’s products work smoothly and effectively.
The Supportability Intelligence team combines machine learning with backend engineering expertise to build exciting products. We work closely with cross-functional partners to solve business problems holistically, where Machine Learning is a critical component but creative thinking and pragmatic problem solving are just as important. As part of a new team, you can have a huge impact on the next generation of our systems from scoping to implementation.
As a Machine Learning Engineer you will design end-to-end solutions, train and deploy new models, and build live production systems as well as data pipelines.
Your work will include:
- Building and maintaining sophisticated ML models to understand users and detect bad actors.
- Designing text classification systems using deep learning to better understand merchants and their websites.
- Imagining new feature ideas and designing real-time data pipelines to incorporate them into our models.
- Building live Stripe systems that interface with our models to make decisions.
- Working with our partner teams to design scalable detection systems that combine human and machine input.
- Working with engineers across the company to develop the right technologies for scaling our infrastructure.
- Defining, measuring and monitoring health & impact metrics.
- Debugging production issues across services and multiple levels of the stack.
You may be a good fit if you:
- Have at least 3 years of software engineering experience.
- Have experience with building and deploying Machine Learning models, especially using NLP and Tensorflow.
- Have experience doing software engineering in a production environment
- Enjoy working cross-functionally, owning a business problem end-to-end.
- Thrive in a collaborative environment involving different stakeholders and subject matter experts.
- Hold yourself and others to a high bar when working with production systems.
- Think about systems and services and write high quality code.
- Bonus if you are experienced in Python, Tensorflow, Scala (Spark), or Ruby, but these are by no means required.