Affirm
Software Engineer II, Backend (ML Training & Serving)
About this role
Join Affirm's ML Training & Serving engineering team as a Software Engineer II to build and operate critical infrastructure for machine learning model training and serving. This role bridges distributed systems, ML infrastructure, and platform engineering, focusing on reliability, developer velocity, and enabling teams across the company.
What you'll do
- Break down larger projects into tasks, deliver in phases, and collaborate to ensure timely completion
- Support product development lifecycle by articulating technical constraints and partnering on risk/trade-off decisions
- Maintain operations and availability through metrics creation, monitoring, and on-call support
- Design, develop, and launch backend systems serving ML infrastructure
- Debug code, provide feedback through code reviews, and navigate large codebases
- Participate in team growth activities including interviewing
What they're looking for
- Backend systems design and development
- Python or Kotlin proficiency
- Distributed systems architecture
- AWS, MySQL, Kubernetes
- Code review and debugging
- Technical communication and collaboration
- Ownership and proactive feedback-seeking
- Testing and extensible code practices
Benefits
- 100% subsidized medical coverage for employee and dependents
- Dental and vision coverage
- Monthly stipends for health, wellness, and tech spending
- Equity rewards
- Remote US position
- Global engineering team collaboration
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Affirm
Affirm builds financial infrastructure and platforms that power point-of-sale lending, merchant operations, card issuance, and capital management at scale. The company is hiring backend engineers, AI solutions engineers, and infrastructure specialists to develop reliable, scalable systems for merchant platforms, payment processing, reliability monitoring, and capital orchestration.
- Website
- affirm.com
Likely interview questions
- Describe a backend system you designed and deployed—what were the key components and how did you ensure reliability?
- How have you approached debugging and fixing issues in a large, unfamiliar codebase?