Affirm
Mid
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