Senior Machine Learning Engineer, Fraud

🕒 vor 2 Monaten

🇨🇦 Kanada – Remote

💵 $153.000 - $213.000 / Jahr

⏰ Vollzeit

🟠 Senior

🤖 Machine-Learning-Entwickler

👻 Geisterscore 2%

infoinfo

🗣️🇺🇸🇬🇧 Englisch erforderlich

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Affirm

1001 - 5000 Mitarbeiter

Gegründet 2012

💳 Fintech

👥 B2C

🛍️ eCommerce

💰 Post-IPO Equity im 2021-01

Fintech • B2C • eCommerce

Affirm ist ein Finanztechnologieunternehmen, das einen "Jetzt kaufen, später zahlen"-Service bietet und es Verbrauchern ermöglicht, Einkäufe zu tätigen und diese über flexible Zahlungspläne im Laufe der Zeit zu bezahlen. Affirm beseitigt versteckte Gebühren und Zinseszinsen und bietet klare Bedingungen für seine Nutzer. Das Unternehmen bietet auch die Affirm-Karte an, eine Debitkarte, die es Benutzern ermöglicht, bei größeren Einkäufen eine Ratenzahlung anzufordern oder kleinere Einkäufe vollständig zu bezahlen. Affirm arbeitet mit verschiedenen Einzelhändlern in mehreren Kategorien zusammen, darunter Elektronik, Bekleidung und Reisen, und bietet Kunden die Bequemlichkeit, sowohl online als auch in physischen Geschäften beim Checkout über Zeit zu zahlen. Die Dienste von Affirm sind in Apple Pay integriert, sodass Kunden Zahlungen nahtlos von ihrem iPhone oder iPad aus vornehmen können.

Beschreibung

• You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data • You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. • You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. • You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. • You will 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. • You will 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.

🎯 Anforderungen

• 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. • You have mastered 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. • You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews. • Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders. • You have strong verbal and written communication skills that support effective collaboration with our global engineering team.

🏖️ Vorteile

• Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents • Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses • Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge • ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount

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