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. Join Affirm as a Machine Learning Engineering Manager and become a pivotal part of our Repayment & Recovery machine learning group. Specifically, you will manage a team of ML engineers that builds the models powering the post-origination side of the credit lifecycle using novel ML techniques and rich representations of data to predict repayment behaviour, prioritise and personalise collections, and minimise loss after a loan is originated. In this role, you will help shape the future of machine learning at Affirm. You'll partner with engineering, product, and risk leaders to design, implement, and scale advanced ML solutions that drive critical capabilities across the company. You will mentor engineers, bring clarity to complex, ambiguous problems, and contribute to a cohesive long-term ML strategy. This role is based in Europe, with openness to candidates in the UK, Poland, or Spain.
What you'll do Set the technical strategy for your team, and help your engineers tie it together with critical, business-impacting projects across repayment, collections, recovery, and loss mitigation. Act as a force-multiplier through your definition and advocacy of technical solutions and operational processes. Collaborate across teams in the product development lifecycle by partnering with product management, design, analytics, and risk to ensure technical sustainability, risks and trade-offs are well understood and managed. Develop talent by providing feedback and guidance, and leading by example. What We Look For Bachelor's in a technical field with 8+ years of industry experience, including 3+ years managing engineers. A background in financial services, specifically in credit / lending, with hands-on experience in post-origination modeling — collections, repayment, recovery, loss mitigation, early-warning or behavioural risk, or similar. Proficiency in machine learning with experience in areas including tree-based models, transformers, deep learning, and agentic ML. Strong engineering skills and the ability to provide hands-on technical leadership while working with our code and architecture. You thrive in ambiguity, and are comfortable moving from low level language idioms all the way to the architecture of large systems to understand how they work. This position requires either equivalent practical experience or a Bachelor's degree in a related field.
