Software Engineer II, Machine Learning (Feature Platform)

About the Engineering team
The Engineering team builds systems that power Affirm’s mission. We take pride in our craft, building reliable, performant, and scalable solutions that touch every part of the business. Whether we’re enabling new ways to pay, ensuring our platforms can scale to the most demanding levels in the industry, or developing seamless servicing experiences, our focus is on solving complex problems through exceptional engineering.

About the team

ML Feature Platform is one of the two teams that compose the ML Platform at Affirm, the other team being ML Training & Serving Platform. The two platform teams work closely together to create the ML + Data flywheel that empowers Affirm.

ML Feature Platform’s mission is to: Grow Affirm’s business by building a delightful, self-serve platform for developing and serving data for machine learning and decisioning.

As the feature platform team, we own multiple artifacts that in aggregate own the end-to-end flows for features. This includes:

• Feature creation, exploration, and serving
• Data storage, availability, and visibility
• Offline backfilling infrastructure and platform improvements

This scope means your work will have a direct impact on the everyday lives and capabilities for our end users: MLEs, Analysts, and decisioning teams. Additionally as the primary data source for ML and online decisioning, our platform’s performance is critical to Affirm’s availability and latency goals.

We’re looking for engineers who are passionate about ML enablement and believe that platform level improvements can bring snowball effects across the company.

What we look for

• You have a total of 1.5+ years of experience as a software engineer.

• You have experience designing, developing and launching backend systems and are proficient in one of Python or Kotlin.

• You are familiar with the building blocks of distributed systems, and the technologies like AWS, MySQL and Kubernetes.

• You have mastered taking a simple problem or business scenario into a so