Senior Machine Learning Engineer; ML Underwriting

Senior Staff Machine Learning Engineer (ML Underwriting)

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 the team as a Senior Staff Machine Learning Engineer and become a pivotal part of our innovative ML team. Our team is dedicated to affirm’s mission of revolutionizing financial services with transparency and inclusivity at its core. We are utilizing advanced machine learning techniques ensuring responsible and accessible financial products.

In this role, you will help shape the future of machine learning ’ll partner with ML Platform, engineering, product, and risk leaders to design, implement, and scale advanced modeling approaches that drive critical decisions across the company. You will elevate our modeling capabilities, influence architectural direction, and ensure our systems can support increasingly sophisticated workloads. You will mentor senior engineers, bring clarity to complex, ambiguous problems, and contribute to a cohesive long‑term ML strategy.

If you are passionate about modern machine learning and excited to drive high‑impact innovation across a growing organization, affirm is the place for you.

• Define and drive multi‑year, multi‑team technical strategy for machine learning across affirm, ensuring alignment with company‑wide priorities and influencing the roadmaps of partner teams and platforms.
• Lead the design, implementation, and scaling of advanced ML systems, setting the architectural direction for complex, cross‑functional initiatives and ensuring systems remain reliable, extensible, and prepared for increasingly sophisticated modeling workloads.
• Partner deeply with ML Platform, product, engineering, and risk leadership to shape long‑term modeling capabilities, define new opportunities for ML impact, and guide infrastructure evolution required for next‑generation ML methods.
• Provide broad technical leadership across the ML organization, mentoring senior engineers, elevating design and code quality, and spreading ML expertise through documentation, talks, and cross‑org guidance.
• Drive clarity and alignment on ambiguous, high‑stakes technical decisions, resolving cross‑team tensions, balancing competing priorities, and exercising judgment optimised for the broader engineering organization.
• Champion operational and system excellence at the area level, owning the long‑term health, availability, and evolution of critical ML systems, and ensuring robust testing, monitoring, and reliability practices across teams.

• 10+ years of experience researching, designing, deploying, and operating large‑scale, real‑time machine learning systems, with a proven record of driving technical innovation and delivering measurable business impact. Relevant PhD can count for up to 2 YOE.
• Experience leading end‑to‑end ML system design, from data architecture and feature pipelines to model training, evaluation, and production deployment. Familiarity with distributed frameworks such as Spark, Ray, or similar large‑scale data processing systems.
• Proficiency in Python and ML frameworks, including PyTorch and XGBoost. Experience with ML tooling for training orchestration, experimentation, and model monitoring, such as Kubeflow, MLflow, or equivalent internal platforms.
• Strong understanding of representation learning and embedding‑based modeling. Deep expertise in neural network‑based sequence modelling, including architectures such as Transformers, recurrent, or attention‑based models, and multi‑task learning systems. Comfortable designing and optimising models that learn from sequential or temporal event data at scale.
• Hands‑on experience with large‑scale distributed ML infrastructure, including streaming or batch data ingestion, feature stores, feature engineering, training pipelines, model serving and inference infrastructure, monitoring, and automated retraining.
• Strong technical leadership: defining long‑term strategy, guiding research direction, and aligning work across teams. Recognised as a trusted expert who can drive clarity and execution even…