Senior Engineering Manager, Machine Learning Platform

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.

We are seeking a Senior Engineering Manager to lead our ML Training & Serving team. This is a senior technical leadership role with company-wide impact. You will directly lead a team of platform engineers, including senior technical leaders, and combine strong people leadership with deep ML infrastructure judgment. In close partnership with senior ICs, you will shape the platform’s strategy and technical direction.

What You'll Do

• Own the technical strategy and roadmap for ML training & serving, covering large-scale model training, model deployment workflows, GPU infrastructure, and low-latency model serving at scale.
• Lead and grow a team of platform engineers while remaining deeply engaged in technical strategy and architectural trade-offs in close partnership with senior ICs.
• Continuously evolve ML training & serving infrastructure to stay ahead of the frontier — anticipating where AI and ML are heading and building the infrastructure that makes those capabilities possible at Affirm before they become urgent needs. This includes large-scale training and serving of transformer-based models, GPU compute, reinforcement learning, and whatever comes next.
• Partner with ML modeling, product, and infrastructure leadership to ensure ML training & serving infrastructure accelerates Affirm's most critical ML initiatives.
• Establish engineering excellence across the organization: reliability, observability, developer experience, and operational rigor.
• Recruit, develop, and retain world-class platform engineers.

What We Look For

• 10+ years of industry experience in software and/or machine learning engineering, with significant hands-on software engineering experience, including 4+ years managing engineering teams.
• Deep expertise in building and operating large-scale ML infrastructure, with substantial experience in one or more of model training systems, model serving, deployment workflows, or GPU infrastructure.
• Strong understanding of ML data needs — training datasets, data quality, reproducibility, evaluation data, and how data shapes model behavior and platform design.
• Fluency with modern ML: deep neural networks, transformer architectures, reinforcement learning, large-scale GPU training and serving.
• Strong systems thinking — comfortable reasoning from low-level infrastructure decisions to broad architectural trade-offs.
• Track record of building platforms that meaningfully accelerate the productivity and impact of ML teams.
• Experience on the applied ML modeling side is a plus — understanding how models are built makes you a