Machine Learning Engineer, Stripe

• We are looking for ML Engineers who are passionate about using ML to improve products and delight customers
• You have experience developing streaming feature pipelines, building ML models, and deploying them to production, even if it involves making substantial changes to backend code
• You are comfortable with ambiguity, love to take initiative, and have a bias towards action
• At least 5 years of industry experience doing end-to-end ML development on a machine learning team and bringing ML models to production
• Advanced degree in a quantitative field (e.g. computer science, statistics, physics, …)
• Proficient in Python, Scala, Spark

Desirable

• The Payments ML Accelerator team is developing capabilities that will unlock the proliferation of ML based techniques across Stripe’s payment products
• We are developing cutting edge deep learning models tailored to Stripe’s payment data, and infrastructure to enable rapid ML exploration and very fast experiment cycles
• We are exploring novel applications powered by ML as well as improving core product features such as detecting fraudulent transactions across all payment methods or optimizing payment acceptance rate
• As a machine learning engineer, you will design and build platforms and services that are configurable and scalable
• You will have the opportunity to build and deploy advanced ML applications and generalizable feature engineering pipelines, with the aim to produce business impact and raise the bar for technical excellence
• You will also have the opportunity to contribute to and influence ML architecture at Stripe
• Build and deploy deep learning architectures and feature embeddings for Payment entities such as merchant, issuer, or customer
• Develop DNN applications and establish the foundation to facilitate increased DNN adoption at Stripe
• Design and architect generalizable ML workflows for rapid expansion of existing ML solutions
• Experiment with advanced ML solutions in the industry and ideate on product applications
• Collaborate with our machine learning infrastructure team to leverage new infra services for business solutions
• Collaborate with data scientists to build ML models