Staff Machine Learning Engineer (Credit International ML)

About this 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. We are looking for a Staff Machine Learning Engineer to be the technical leader building high-quality production-grade models powering credit lending decisions in countries across Europe and in Australia. You will use 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 how machine learning fuels Affirm’s global expansion. 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 You will be responsible for setting technical strategy for your team on a year-long time scale, and help your team tie it together with critical, business-impacting projects.

You will collaborate with ML Platform, product, engineering, and risk leadership to shape long-term modeling capabilities needed for scalable international expansion. You will act as a force-multiplier for your team through your definition and advocacy of technical solutions and operational processes. You will foster a culture of quality and ownership on your team by setting code review and design standards. You will help develop talent on your team by providing feedback and guidance, and leading by example.

What we look for 8+ years of experience researching, designing, deploying, and operating large-scale, real-time machine learning systems. A relevant PhD can count for up to 2 YOE. Strong Python skills and experience writing production-quality code. Experience building and evaluating models for tabular classification problems (including gradient-boosted decision trees like LightGBM/XGBoost, and deep learning frameworks like PyTorch).

Experience working with distributed data processing or parallel compute frameworks (e.g. Spark, Ray). Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow). Proficient in using AI-powered developer tools (e.g., Claude Code, Codex, Cursor) to accelerate the model development lifecycle. Strong engineering skills and the ability to provide hands-on technical leadership while working with our code and architecture.