Lead an ML engineering team building post-origination credit risk models at Affirm

A people-management role leading a team of ML engineers building models that predict repayment behavior, personalize collections, and minimize loss after loan origination at Affirm. Requires 8+ years of technical experience including 3+ years managing engineers, plus hands-on background in credit/lending post-origination modeling. Europe-based (UK, Poland, or Spain).

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What this interview tests

Team leadership and technical strategy-setting for ML engineers
Post-origination credit modeling (repayment, collections, recovery, loss mitigation)
ML technique breadth: tree-based models, transformers, deep learning, agentic ML
Cross-functional alignment with product, risk, and analytics
Mentorship and engineer development

Common question themes

How would you set technical strategy and prioritize projects for a repayment/recovery ML team?

Walk me through a modeling approach for predicting repayment or prioritizing collections

Tell me about a time you acted as a force-multiplier through a technical or process decision

Describe aligning product, risk, and engineering on a tradeoff involving model behavior

How do you mentor and develop ML engineers on your team?

How do you move between low-level implementation details and large-system architecture?

How candidates describe it

Real Manager interview stories — retold from candidates' public write-ups, with sources.

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