Software Engineer, Backend (Machine Learning Platform)
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Affirm proudly includes Returnly.
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
Location - Remote U.S.
Grade - USA29
Affirm is proud to be a remote-first company! The majority of our roles are remote and can be located anywhere in the U.S. and Canada (with the exception of the U.S. Territories, Quebec, Yukon, Nunavut, and the Northwest Territories) unless the job indicates a different global location. We are currently building operations in Spain, Poland, and Australia. Employees in remote roles have the option of working remotely or from an Affirm office in their country of hire, and may occasionally travel to an Affirm office or elsewhere for required meetings or team-building events. Our offices in Chicago, New York, Pittsburgh, Salt Lake City, San Francisco and Toronto will remain operational and accessible for anyone to use on a voluntary basis, subject to local COVID-19 guidelines.
All full-time jobs at Affirm (excluding interns and apprentices) are tied to a transparent grade-based pay range taking location into account.
If you got this far, we hope you're feeling excited about this role. Even if you don't feel you meet every single requirement, we still encourage you to apply. We're eager to meet people who believe in Affirm's mission and can contribute to our team in a variety of ways—not just candidates who check all the boxes.
At Affirm, People Come First is one of our core values, and that’s why diversity and inclusion are vital to our priorities as an equal opportunity employer. You can read about our D&I program here and our progress thus far in our 2021 DEI Report.
We also believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.
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