Staff Software Engineer, Backend (ML Platform)

The ML Platform team is on a mission to build the core infrastructure that powers Affirm's intelligence.  Affirm uses machine learning to assess and approve each BNPL transaction. Affirm’s ML models answer questions like: is the applicant willing and able to repay this loan? 

The role of the ML Platform team is to build the compute platform for training and serving all of Affirm's ML models and features. Online, we operate a feature store and model server that enable real-time feature computation and model scoring. Offline, we manage an environment for running large scale model training and data analysis.

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What You'll Do
· You will be responsible for setting technical strategy for your team on a year-long time scale, and help your team tie it together with critical, business-impacting projects.
· You will collaborate across teams in the ML development lifecycle by collaborating with machine learning engineers, platform engineers, and product management to ensure technical sustainability, risks and trade-offs are well understood and managed.
· You will act as a force-multiplier for your team through your definition and advocacy of technical solutions and operational processes.
· You take ownership of your team’s operations and availability by ensuring you have the right monitoring, triage rotations, playbooks, testing and alerting in place to support “keep the lights on” & on-call efforts.
· You will foster a culture of quality and ownership on your team by setting code review and design standards for your team, and advocating for them beyond your team through your writing and tech talks.
· You will help develop talent on your team by providing feedback and guidance, and leading by example

What We Look For

·You have 8+ years of experience designing, developing and launching backend systems at scale using languages like Python or Kotlin.
· You have an extensive track record of developing highly available distributed systems using technologies like AWS, MySQL, Spark and Kubernetes.
· You have experience building and operating online, real-time ML infrastructure like a model server or a feature store

· You have experience developing an offline environment for large scale data analysis and model training using technologies like Spark, Kubeflow, Ray, and Airflow
· You have experience delivering major features, system components or deprecating existing functionality in a system through the definition of a technical and execution plan. You write high quality code that is easily understood and used by others.
· You thrive in ambiguity, and are comfortable moving from low level language idioms all the way to the architecture of large systems to understand how they work.
· Your growth and impact trajectory demonstrates that you have mastered gathering and iterating on feedback from your engineering and cross-functional peers.
· You have strong verbal and written communication skills that support effective collaboration with our global engineering team
· This position requires either equivalent practical experience or a Bachelor’s degree in a related field.

Base Pay Grade - P

Equity Grade - USA 13

Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.

Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)

USA base pay range (CA, WA, NY, NJ, CT) per year: $225,000 - $275,000
USA base pay range (all other U.S. states) per year: $200,000 - $250,000


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