Software Engineer II, Backend (Infrastructure Platform)

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. They are looking for a Software Engineer to design and build a next-generation reliability platform for Affirm’s production systems, blending traditional distributed systems engineering with AI-assisted development. The role involves building a centralized reliability command center, creating AI agents for incident triage, and collaborating with partner teams to deliver effective solutions.

Responsibilities:

• Build a centralized reliability command center that gives teams a unified view of system health, risk, and reliability across services and environments
• Create AI agents that can assist with incident triage, root-cause exploration, log/trace summarization, and recommended next actions
• Create delightful developer-facing features and APIs that help engineers explore data, debug issues, and make better decisions
• Use AI-assisted development tools (e.g., Cursor, Claude, Copilot-like tools) as leverage to prototype, refactor, and ship high-quality code quickly
• Own projects end-to-end: requirements, architecture, implementation, testing, rollout, and iteration based on feedback
• Collaborate closely with partner teams (product, infra, data, SRE) to understand pain points and translate them into simple, powerful solutions

Requirements:

• This position requires either equivalent practical experience or a Bachelor's degree in a related field
• 1.5+ years of experience as a software engineer
• Strong proficiency in Python, with experience architecting data-intensive applications and robust APIs
• Problem-solving and product sense: you care about the 'what' and the 'why,' not just the 'how.' You are capable of taking ambiguous requirements and rapidly iterating towards a working solution
• Hands-on experience with AI-assisted development, such as Cursor, Claude, or similar tools, and enthusiasm for using them to build and ship features faster
• Practical use of LLMs or AI frameworks to enhance automation and guidance with guardrails and citations