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.
Machine Learning is central to how Affirm delivers on its mission — powering decisions across underwriting, fraud, servicing, and personalization. We are seeking a Senior Manager to lead our ML Platform engineering organization, the team that builds and operates the critical infrastructure enabling every ML capability at Affirm . This is a high-impact, technically demanding role at the intersection of distributed systems, modern AI, and platform engineering. You will manage a team of engineering managers and stay deeply connected to the engineers building the platform, ensuring we continuously evolve ahead of Affirm 's ML ambitions.
What You'll Do
- Own the technical strategy and roadmap for ML Platform, covering real-time and batch feature computation, model training infrastructure, and model serving at scale.
- Lead and grow a team of engineering managers, while staying hands-on with the technical direction and maintaining close partnership with ICs.
- Continuously evolve the platform 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 the platform 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
- 12+ years of industry experience in software and/or machine learning engineering, with significant hands-on software engineering experience, including 4+ years managing engineering managers.
- Deep expertise in building and operating large-scale ML infrastructure — feature stores, model serving systems, training pipelines, or equivalent.
- Strong understanding of data — data pipelines, data quality, 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 better platform builder.
- Experience navigating ambiguity a