The ML Platform team’s mission is to develop a self-service foundation for applications of machine learning that will provide scalable business impact. This team works in close partnership with ML engineers to deliver systems that enable fast paced ML development. Affirm’s goal is to build the largest network as measured by the number of users and merchants. Machine learning is a critical tool in this effort.

We are looking for highly motivated engineers to help. Come join us!

What you'll do

• Design and build Affirm’s next generation ML model server, API gateway, and observability framework
• Evaluate and onboard pioneering MLOps tools and frameworks that help ML engineers launch their models with speed and accuracy
• Develop a platform for training and serving ML models at scale across multiple regions
• Design self-service and user-friendly tools, APIs, and services that power all of ML at Affirm
• Partner with ML engineers, product engineers, and platform infrastructure engineers to launch impactful new initiatives; your systems will launch models that decide who we lend to in real time
• Serve as a trusted advisor on the application and implementation of ML across Affirm

What we look for

• 3 years of experience
• Strong engineering background and demonstrated experience with building data infrastructure and real-time, distributed systems
• Experience with MLOps frameworks like Kubeflow, Ray, KServe, MLflow and SageMaker
• Experience with data frameworks such as Spark, Kafka, Airflow, and Flink
• Experience with container orchestration frameworks like Kubernetes
• Experience with cloud service providers such as AWS
• Experience with Python
• Interest in ML models
• Excellent written and oral communication skills including the capability to drive requirements with product and engineering teams and to present technical concepts and results in an audience-appropriate way
• Ability to work efficiently both solo and as part of a team; willingness to learn new things and mentor others
• Passion to change consumer banking for the better, while developing a deeper understanding of machine learning systems