Senior Staff Machine Learning Engineer, (Machine Learning)

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. As a Senior Staff Machine Learning Engineer, you will shape the future of machine learning at Affirm by leading the design and implementation of advanced ML systems and mentoring senior engineers.

Responsibilities:

• You will 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
• You will 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
• You will 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
• You will 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
• You will drive clarity and alignment on ambiguous, high-stakes technical decisions, resolving cross-team tensions, balancing competing priorities, and exercising judgment optimized for the broader engineering organization
• You will 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

Requirements:

• 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. Use distributed frameworks such as Spark, Ray, or similar large-scale data processing systems
• Proficient in Python and ML frameworks, including PyTorch and XGBoost. Experienced 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. Possess deep expertise in neural network-based sequence modeling, including architectures such as Transformers, recurrent, or attention-based models, and multi-task learning systems. Comfortable designing and optimizing models that learn from sequential or temporal event data at scale
• Deep 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
• Provide strong technical leadership: defining long-term strategy, guiding research direction, and aligning work across teams. Recognized as a trusted expert who can drive clarity and execution even in ambiguous problem spaces
• Demonstrate exceptional judgment, collaboration, and communication skills, enabling effective technical discussions with engineers, researchers, and executives. Mentor senior engineers, foster technical excellence, and contribute to a culture of continuous learning
• Strong verbal and written communication skills that support effective collaboration across our global engineering organization
• Equivalent practical experience or a Bachelor's degree in a related field