About the role
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.
About the Team:
People Tech & Analytics (PTA) builds and owns the data, AI, and technology infrastructure for Affirm's People function. The team runs like a product engineering group embedded in HR. We own the full stack: data ingestion, transformation, dashboards, AI tools, and production applications deployed on Snowflake.
About the Role:
This is a hands-on engineering role. You will build, deploy, and maintain AI-powered systems that serve the People function and the broader employee base. The work is taking messy business problems (fragmented knowledge, manual processes, disconnected tools) and turning them into working software: agents, APIs, applications, and infrastructure. You will work closely with partners across the People function who own domain expertise and stakeholder relationships. Your job is to turn their rough applications and processes into production systems, and to push back when a technical constraint changes what's possible. This is not a pure backend role. You will be in the room when business problems are being scoped, and you need to understand the problem well enough to make architecture decisions on your own. You also need to take what you build and get it running in production. Hosting, security, deployment, and ongoing maintenance are all part of the job.
What You'll Do:
- Build and ship AI agents, APIs, and applications on Affirm's internal platform (Snowpark Container Services / Quicksilver). You own the full lifecycle: architecture, containerization, networking, secrets, CI/CD, monitoring, and fixing what breaks.
- Turn messy business requirements from People Operations stakeholders into production systems. Integrate with Workday, Notion, and case management tools so AI surfaces real answers from governed content, not model guesses.
- Navigate Affirm's existing security and data governance infrastructure to get AI systems running safely on people data. RBACs, data classification, and access policies already exist, but connecting them across systems (Workday, Snowflake, case tools) is where it gets messy. You figure out what's allowed, build within those constraints, and make sure employee data stays where it's supposed to.