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. Join the Affirm 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 at Affirm. You’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. What you’ll do - 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. What we look for - You have 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. - You have experience leading end-to-end ML system design, from data architecture and feature pipelines to model training, evaluation, and production deployment. You use distributed frameworks such as Spark, Ray, or similar large-scale data processing systems. - You are proficient in Python and ML frameworks, including PyTorch and XGBoost. You are experienced with ML tooling for training orchestration, experimentation, and model monitoring, such as Kubeflow, MLflow, or equivalent internal platforms. - You have a strong understanding of representation learning and embedding-based modeling. You possess deep expertise in neural network-based sequence modeling, including architectures such as Transformers, recurrent, or attention-based models, and multi-task learning systems. You are comfortable designing and optimizing models that learn from sequential or temporal event data at scale. - You have 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. - You provide strong technical leadership: defining long-term strategy, guiding research direction, and aligning work across teams. You are recognized as a trusted expert who can drive clarity and execution even in ambiguous problem spaces. - You demonstrate exceptional judgment, collaboration, and communication skills, enabling effective technical discussions with engineers, researchers, and executives. You mentor senior engineers, foster technical excellence, and contribute to a culture of continuous learning. - You have strong verbal and written communication skills that support effective collaboration across our global engineering organization. - This position requires equivalent practical experience or a Bachelor’s degree in a related field. Pay Grade - R Equity Grade - 9 Employees new to Affirm typically come in at the start of the pay range

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. Proficiency 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, including neural network architectures such as Transformers, recurrent, or attention-based models, and multi-task learning systems. 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, ability to define long-term strategy, guide research direction, and align work across teams. Excellent collaboration and communication skills, capable of engaging with engineers, researchers, and executives. Bachelor’s degree or equivalent practical experience in a related field.

Define and drive multi-year, multi-team technical strategy for machine learning across the company, aligning with priorities and influencing roadmaps of partner teams. Lead design, implementation, and scaling of advanced ML systems, setting architectural direction for cross-functional initiatives and ensuring reliability, extensibility, and readiness for sophisticated workloads. Partner with ML Platform, product, engineering, and risk leadership to shape long-term modeling capabilities, identify opportunities for ML impact, and guide required infrastructure evolution. Provide broad technical leadership across the ML organization, mentoring senior engineers, elevating design and code quality, and disseminating ML expertise through documentation, talks, and cross-org guidance. Drive clarity in ambiguous, high-stakes technical decisions; resolve cross-team tensions and balance priorities for the broader engineering organization. Champion operational and system excellence, owning long-term health, availability, and evolution of ML systems, with robust testing, monitoring, and reliability practices across teams.