Affirm currently has 19 active job listings related to artificial intelligence. The majority of these roles are focused on serving infrastructure, accounting for 32% of the openings, followed closely by data, application, and agents, each representing 26% or 21% of the listings. Engineering is the primary function hiring for these positions, with the United States being the dominant hiring country. Key technical areas include model serving, inference infrastructure, and agent orchestration.
Affirm currently has 23 active AI-related roles in our index. The most common open titles are: Machine Learning Engineer II (2), Manager, Machine Learning Engineering (Fraud) (2), Manager, Machine Learning Engineering (Underwriting) (2), People Knowledge Experience Manager (2), Senior Staff Machine Learning Engineer, (ML Underwriting) (2). Most positions are in Engineering and Product.
Affirm's active AI hiring is concentrated in: agents (30%), serving infrastructure (26%), application (22%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Affirm is hiring AI talent in: United States (20 roles), Canada (3 roles).
Job postings at Affirm most frequently reference: model serving, agent orchestration, inference infra, rag, llm observability.
In the past 30 days, Affirm has posted 6 new AI-related roles. That is a -57% change versus the prior 30 days (14 → 6).
Currently tracking 11 active AI roles, down 45% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $124k–$310k (avg $233k).
| Title | Stage | AI score |
|---|---|---|
| Analyst II, Full Stack This role focuses on developing and optimizing fraud decisioning strategies within a fintech company. It involves extensive data analytics, collaborating with cross-functional teams (Product, Engineering, Operations, Finance), and developing new fraud features. A key responsibility is creating scalable frameworks for proprietary fraud machine learning models and evaluating data sources to mitigate fraud risk. The role also involves partnering with the Machine Learning team on fraud and identity verification strategies and owning the end-to-end analytics workflow, including defining metrics and creating dashboards. | Data | 7 |