Senior Manager, Machine Learning Engineering

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

- 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. 

What we look for

- 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

- 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.

- This position requires either equivalent practical experience or a Bachelor’s degree in a related field

Pay Grade - R
Equity Grade - 15

Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.

Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)

USA base pay range (CA, WA, NY, NJ, CT) per year: $260,000 - $310,000
USA base pay range (all other U.S. states) per year: $232,000 - $282,000