Machine Learning at Cash App has swiftly grown to become a central function of the business, with over 120 people working on solving some of the most cutting edge ML questions in FinTech. Our team is continuing to grow and we’re looking for Engineers with experience developing ML pipelines and infrastructure to join us and build out the next generation platform to power use cases across the company. Across the team, we employ methods ranging from simple heuristics to Deep Learning technology to improve our performance and services. We’re also excited to explore new directions with the team to continue pushing the boundaries of what’s possible.

Our ML Engineers operate embedded in product-focused Workstreams and work in close collaboration with Data Scientists, ML Modelers and Software Engineers from across Cash App.

Problems you’d work on:

• Design elegant ML pipelines and services as well as prototype new approaches and productionize solutions at scale for our 36+ million active users
• Create a world class platform for training, hosting and maintaining ML models
• Apply ML and engineering best practices to shape how Cash App develops, tests and maintains ML-platform solutions
• Help design and execute our long-term Machine Learning strategy across Cash App

You have: 

• A degree in Computer Science, Computer Engineering, or a related technical field. A graduate degree is a nice to have
• 3+ years of experience in software development and demonstrated technical initiative and leadership on previous projects
• Curiosity and a passion for Cash App’s mission of economic empowerment
• Experience helping your teammates grow through mentorship and providing constructive feedback
• A desire to be hands-on and help invent the future of AI
• The ability to work autonomously in the rapidly evolving world of ML
• Experience or knowledge in ML is helpful but not a prerequisite: if you are a solid software engineer, then we are happy to train you up in the mysterious ways of ML

Stack: 

We value general engineering skill and versatility over existing domain knowledge, but here’s a sample of what we currently use:

• Python, Java and Kotlin
• Tensorflow and PyTorch
• AWS, GCP, and Kubernetes
• MySQL, DynamoDB, Kafka, Apache Beam and Google DataFlow