Software Engineer Ii, Backend (ml Training & Serving)

Affirm Affirm · Fintech · United States · Remote · Checkout

Software Engineer II, Backend (ML Training & Serving) at Affirm. This role focuses on building and operating the critical infrastructure for ML model training and serving, sitting at the intersection of distributed systems, ML infrastructure, and platform engineering. The role requires experience in backend systems, distributed systems building blocks (AWS, MySQL, Kubernetes), and proficiency in Python or Kotlin.

What you'd actually do

  1. With the support of your team’s tech lead and manager, you will break down larger projects into individual tasks, deliver them in multiple phases, and collaborate with others to ensure timely delivery of your work.
  2. You will support your peers and stakeholders in the product development lifecycle by collaborating across engineering, articulating technical constraints, and partnering on decisions that properly consider risks and trade-offs.
  3. You will support the operations and availability of your team’s artifacts by creating and monitoring metrics, escalating when needed, and supporting “keep the lights on” & on-call efforts.
  4. You will contribute to a sense of community on your team by engaging in growth and development activities such as participation in the interview process.

Skills

Required

  • 1.5+ years of experience as a software engineer
  • Designing, developing and launching backend systems
  • Python or Kotlin
  • Familiar with the building blocks of distributed systems
  • AWS
  • MySQL
  • Kubernetes
  • Taking a simple problem or business scenario into a solution that interacts with multiple software components
  • Writing clear, easily understood, well tested and extensible code
  • Navigating a large code base
  • Debugging others' code
  • Providing feedback to other engineers through code reviews
  • Ownership of your growth
  • Proactively seeking feedback
  • Strong verbal and written communication skills

What the JD emphasized

  • ML Training & Serving engineering team
  • critical infrastructure that enables models to be trained and served
  • intersection of distributed systems, ML infrastructure, and platform engineering
  • building reliable shared platforms
  • improving developer velocity
  • creating durable systems

Other signals

  • ML infrastructure
  • training and serving
  • distributed systems
  • platform engineering