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. On the ML Fraud team, you’ll build and improve machine learning systems that make real‑time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion. You’ll work closely with experienced ML engineers, platform partners, and cross‑functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as fraud patterns evolve. What you’ll do Lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data Build and scale feature pipelines and training datasets from proprietary and third‑party signals, partnering with data and platform teams when needed Prototype new modeling ideas and features, run offline experiments, and drive the best‑performing approaches into production with appropriate risk controls Productionize models: integrate into batch and/or real‑time decision systems, and improve reliability, latency, and operational robustness Instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve Identify and implement foundational improvements to how the team builds models Collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non‑technical audiences What we look for 6+ years experience researching, training, tuning and launching ML models at scale. Relevant PhD can count for up to 2 years of experience Track record of delivering high‑impact machine learning models in a low‑latency live setting Strong Python skills and experience writing production‑quality code Experience building and evaluating models for tabular classification problems (preferably gradient‑boosted decision trees like LightGBM/XGBoost/CatBoost, or similar) Experience with a deep learning framework (PyTorch preferred) Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar) Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms) Proficient in using AI‑powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day‑to‑day development workflows Mastery of taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well‑tested and extensible code Comfortable navigating a large code base, debugging others’ code, and providing feedback to other engineers through code reviews Proactive ownership of growth, seeking feedback from team, manager, and stakeholders Strong verbal and written communication skills that support effective collaboration with our global engineering team Location Remote Canada Pay & Benefits Pay Grade: N Equity Grade: 6 Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). Employees may be eligible for equity rewards offered by affirm holdings Inc. Benefits: health care coverage (Affirm covers all premiums for all levels for you and your dependents), flexible spending wallets for technology, food, lifestyle and family forming expenses, competitive vacation and holiday schedules, ESPP (employee stock purchase plan). EEO & Accommodations We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. For U.S. positions that could be performed in Los Angeles or San Francisco, Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, we will consider for employment qualified applicants with arrest and conviction records. #J-18808-Ljbffr