Senior Manager, Machine Learning Engineering

Affirm

Senior Manager leading Machine Learning Engineering for fraud detection at Affirm. Collaborating with teams to build and enhance ML systems for real-time transaction decisions.

Posted 4/22/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $232,000 - $310,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowPythonPyTorchRaySpark

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningPythontabular classificationgradient-boosted decision treesLightGBMXGBoostCatBoostdeep learningPyTorchdistributed data processing

Soft Skills

communicationcollaborationproblem-solvingownershipfeedbackdebuggingcode reviewsproactive learningclear codingextensibility

Certifications

PhD in related fieldBachelor’s degree in related field