🛠 Tech Stack

💼 About This Role

You'll build and improve machine learning systems for real-time fraud detection at Affirm, protecting consumers and merchants. You'll take models from idea to production, balancing fraud loss, customer experience, and conversion. You'll collaborate with experienced ML engineers and cross-functional stakeholders to develop fraud prediction models and monitor model health as fraud patterns evolve.

🎯 What You'll Do

• Develop and iterate on fraud prediction models for tabular and behavioral data
• Build and scale feature pipelines and training datasets from proprietary and third-party signals
• Prototype new modeling ideas, run offline experiments, and drive best-performing approaches into production
• Instrument and monitor model and data health, define retraining/backtesting workflows

📋 Requirements

• 2+ years of experience as an ML engineer or a PhD in a relevant field
• Strong Python skills and production-quality code
• Experience building gradient-boosted decision trees for tabular classification (LightGBM/XGBoost/CatBoost)
• Experience with a deep learning framework (PyTorch preferred)

✨ Nice to Have

• Experience with distributed data processing (Spark, Ray, Dask)
• Experience with ML lifecycle tooling (Kubeflow, Airflow, MLflow)
• Proficient in AI-powered developer tools (Claude Code, Cursor)

🎁 Benefits & Perks

• 🏥 100% subsidized medical, dental, and vision coverage for you and dependents
• 💻 Tech & lifestyle stipends for Technology, Food, and Wellness
• 📅 Competitive vacation and holiday schedules
• 🏦 Employee stock purchase plan (ESPP) at a discount

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min