Build Affirm's AI-assisted reliability command center for production systems

This is a hands-on backend engineer role building a next-gen reliability platform at Affirm — a centralized command center plus AI agents for incident triage and root-cause exploration. Expect questions on Python API/data-system design, fast iterative 'vibe coding' with AI tools, and where LLM automation needs guardrails.

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What this interview tests

Python backend architecture for data-intensive apps and APIs
AI-assisted development workflow (Cursor/Claude/Copilot) at a high quality bar
Designing AI agents for incident triage and root-cause exploration with guardrails/citations
End-to-end ownership: requirements through rollout and iteration
Cross-team collaboration with product, infra, data, SRE
Reliability platform / observability tooling design

Common question themes

Design a centralized reliability dashboard aggregating health signals across services

How would you build an AI agent to assist incident triage without giving wrong root causes

Walk through your workflow using AI coding tools to prototype and ship a feature quickly

How do you add guardrails and citations to an LLM-powered automation feature

Tell me about taking an ambiguous reliability pain point from an SRE team to a shipped solution

Architecting a robust API for engineers to explore incident/debug data

How candidates describe it

Real Software Engineer II interview stories — retold from candidates' public write-ups, with sources.

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