Manager, Machine Learning Engineering, Checkout

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.

What you'll do

• 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 & Benefits

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 which is based on a variety of 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

#LI-Remote

Remote-first with flexibility built in

Affirm is proud to be a remote-first company. 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.

Benefits designed for you

Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights:

• Health coverage at no cost: We cover 100% of premiums for employees and their dependents.
• Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you.
• Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to.
• Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount.

We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help.

For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law.

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