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.
Currently tracking 11 active AI roles, down 45% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $124k–$310k (avg $233k).
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).
| 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 |
| Analyst II, Full Stack (Customer Servicing Analytics) This role focuses on building and maintaining forecasting models using time series, regression, and machine learning for operational planning within a fintech company. The analyst will apply AI/LLM tools across the analytics workflow, from data exploration to stakeholder communication, and contribute to shaping the team's AI usage. Key responsibilities include demand forecasting, headcount planning, and surfacing efficiency improvements, requiring strong analytical skills, SQL, Python/R, and cloud data platform experience. |
| Data |
| 5 |
| People Knowledge Experience Manager This role focuses on building a centralized Employee Experience (EX) Hub, leveraging AI to enhance support and knowledge management within the People Operations function. The primary goal is to establish an AI-ready knowledge ecosystem, design integrated systems and workflows, and implement AI-enabled tools for efficient and compliant employee support, while managing operational and regulatory risks in a regulated HR environment. | Data | 5 |
| Staff Analytics Engineer, Subledger Platform Staff Analytics Engineer to build and own the Financial Subledger Data Platform using dbt and Snowflake. This role involves creating dbt models, implementing data quality and controls, and embedding AI-assisted reconciliation capabilities. The engineer will also own subledger data products, partner with accounting teams, drive operational ownership, and collaborate with upstream engineering teams. The role includes coaching another engineer. | Data | 5 |
| Staff Analytics Engineer, Subledger Platform Staff Analytics Engineer role focused on building and owning the Financial Subledger Data Platform using dbt and Snowflake. The role involves creating dbt models, implementing data quality and controls, and embedding AI-assisted reconciliation capabilities. It requires strong SQL, data modeling, Python, and Snowflake expertise, with a focus on production ownership and mentoring. | Data | 5 |