Affirm

Manager, Machine Learning Engineering, Checkout

In short

This role leads a team of ML engineers building models that power credit lending decisions across Europe and Australia at Affirm, a fintech company. It involves setting technical strategy, hands-on technical leadership, and partnering with product, risk, and analytics teams, while mentoring engineers. It requires 8+ years of industry experience including 3+ years managing engineers, a background in credit/lending financial services, and proficiency in ML techniques such as tree-based models, transformers, deep learning, and agentic ML. The role is UK remote.

About the Role
At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. Join Affirm as a Machine Learning Engineering Manager and become a pivotal part of our International Credit Machine Learning group. Specifically, you will manage a team of ML engineers that builds the models powering credit lending decisions in countries across Europe and in Australia. You will lead your team in using novel ML techniques and rich data representations to predict repayment behavior and optimize how we offer loans to our borrowers. 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.
Responsibilities
•Set the technical strategy for your team, and help your engineers tie it together with critical, business-impacting projects in our underwriting system
•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
•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
Compensation & Conditions
•Base Pay Grade - P, Equity Grade - 7
•Employees new to Affirm typically come in at the start of the pay range
•Affirm focuses on providing a simple and transparent pay structure based on factors including location, experience and job-related skills
•Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidised medical coverage, dental and vision for you and your dependents)
•Base pay range per year: £142,000 - £190,000
•Location: UK Remote
•Remote-first with flexibility built in: most roles can be done from almost anywhere within the country of employment; some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work
•All new hires will be invited to attend an in-person onboarding experience
•Health coverage at no cost: 100% of premiums covered for employees and their dependents
•Spending stipends: monthly stipends support tech setup and health and wellness options
•Time off to recharge: flexible time off and generous holiday calendars
•Own a piece of what you build: employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount

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