Senior Staff Machine Learning Engineer,

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 mission is to revolutionize financial services with transparency and inclusivity at its core. We utilize advanced machine learning techniques to ensure responsible and accessible financial products. What You'll Do Define and drive multi‑year, multi‑team technical strategy for machine learning across the company, ensuring alignment with priorities and influencing partner roadmaps. Lead design, implementation, and scaling of advanced ML systems, setting architectural direction for complex, cross‑functional initiatives. Partner deeply with ML Platform, product, engineering, and risk leaders to shape long‑term modeling capabilities and guide infrastructure evolution needed for next‑generation ML methods. Provide broad technical leadership across the ML organization, mentoring senior engineers, elevating design and code quality, and spreading 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 optimized for the engineering organization. Champion operational and system excellence, owning health, availability, and evolution of critical ML systems, ensuring robust testing, monitoring, and reliability practices. What We Look For 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 measurable business impact. PhD may 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, using distributed frameworks such as Spark, Ray, or similar. Proficiency in Python, ML frameworks (PyTorch, XGBoost), and ML tooling for training orchestration, experimentation, and model monitoring (Kubeflow, MLflow, or equivalent). Strong understanding of representation learning and embedding‑based modeling; deep expertise in neural‑network sequence modeling (Transformers, recurrent, attention‑based), multi‑task learning and optimization for sequential or temporal event data at scale. Hands‑on experience with large‑scale distributed ML infrastructure: streaming/batch data ingestion, feature stores, training pipelines, model serving, monitoring, and automated retraining. Strong technical leadership: defining long‑term strategy, guiding research, aligning work across teams, recognized as a trusted expert. Exceptional judgment, collaboration, and communication skills, enabling effective technical discussions with engineers, researchers, and executives; mentoring senior engineers, fostering continuous learning. Strong verbal and written communication skills to support collaboration across a global engineering organization. Requires equivalent practical experience or a Bachelor’s degree in a related field. Pay Grade – R. Equity Grade – 9. Base pay range: $206,000 – $256,000 per year. Additional benefits include monthly stipends for health, wellness and tech spending, and 100% subsidized medical coverage, dental and vision for you and your dependents. Employees may be eligible for equity rewards offered by the parent company. Benefits Health‑care coverage – company covers all premiums for all levels of coverage for you and your dependents. Flexible Spending Wallets – generous stipends for technology, food, lifestyle needs and family‑forming expenses. Time off – competitive vacation and holiday schedules. ESPP – employee stock purchase plan enabling you to buy shares at a discount. Affirm is committed to providing an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations during the hiring process. By submitting an application, you acknowledge that you have read our Global Candidate Privacy Notice and freely and unambiguously give consent to the collection, processing, use, and storage of your personal information as described. Referrals increase your chances of interviewing at the company by 2×. #J-18808-Ljbffr