Senior Machine Learning Engineer (Fraud)

About This RoleAI processing…

Key Responsibilities

• 1
  You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data
• 2
  You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.
• 3
  You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
• 4
  You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve.
• 5
  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.
• 6
  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.

Requirements

• You have 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
• Experience with a deep learning framework (PyTorch preferred).
• 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.
• You have strong verbal and written communication skills that support effective collaboration with our global engineering team.
• What we look for - You have 6+ years experience researching, training, tuning and launching ML models at scale.
• Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office.
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Perks & Benefits