Senior Staff Machine Learning Engineer,

Senior Staff Machine Learning Engineer (ML Underwriting) Affirm, headquartered in Queens, Prince Edward Island, Canada, is reinventing credit to make it more honest and friendly, giving consumers flexibility to buy now and pay later without hidden fees or compounding interest. The Senior Staff Machine Learning Engineer will play a pivotal role in our advanced ML team, helping shape the future of machine learning across the organization. Location Queens, Prince Edward Island, Canada (remote-first; occasional on-site visits may be required) Responsibilities In this role, you will help shape the future of machine learning at Confirm. 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. What You’ll Do Define and drive multi-year, multi-team technical strategy for machine learning across the organization, aligning with company-wide priorities and influencing partner roadmaps. Lead the design, implementation, and scaling of advanced ML systems, setting architectural direction for large-scale initiatives and maintaining reliability and extensibility. Collaborate deeply with ML Platform, product, engineering, and risk leadership to shape long-term modeling capabilities, identify new opportunities for impact, and guide infrastructure evolution for next-generation ML methods. 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 and alignment on ambiguous, high‑stakes technical decisions, resolving cross-team tensions and balancing competing priorities with optimized judgment for the broader engineering organization. Champion operational and system excellence, owning the long‑term health, availability, and evolution of critical ML systems and enforcing 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. Relevant PhD may count for up to 2 years of experience. 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 and ML frameworks, including PyTorch and XGBoost, with expertise in ML tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, MLflow). Strong knowledge of representation learning and embedding‑based modeling, and deep expertise in neural network‑based sequence modeling (Transformers, recurrent, or attention‑based models) and multi‑task learning systems. Hands‑on experience building large‑scale distributed ML infrastructure, covering streaming or batch data ingestion, feature stores, training pipelines, model serving, monitoring, and automated retraining. 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 in ambiguous problem spaces. Exceptional judgment, collaboration, and communication skills, enabling effective technical discussions with engineers, researchers, and executives, and fostering a culture of continuous learning. Excellent verbal and written communication skills to collaborate across our global engineering organization. Equivalent practical experience or a bachelor’s degree in a related field; a relevant PhD is considered. Pay & Equity Pay Grade: REquity Grade: 9Annual base pay range: $206,000 – $256,000 (based on location, experience, and job-related skills). Monthly stipends may be available for health, wellness, and tech spending, and equity rewards are offered by the parent company. Benefits Health care coverage – premiums are fully covered 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 that allow you to rest and recharge. Employee Stock Purchase Plan (ESPP) – opportunity to buy shares at a discount. Affirm is proud to be a remote‑first company; most roles are remote, with occasional on‑site requirements for a limited set of positions. We believe it’s On Us to provide an inclusive interview experience for all, including people with disabilities. We provide reasonable accommodations to candidates needing individualized support during the hiring process. By clicking “Submit Application,” you acknowledge you have read Confirm’s Global Candidate Privacy Notice and freely give informed consent to the collection, processing, use, and storage of your personal information as described therein. #J-18808-Ljbffr