Senior Manager, Machine Learning Engineering

About the Role

The role is based on remote work in Canada, offering the flexibility and autonomy that comes with working from home. This position requires a blend of technical expertise, leadership skills, and the ability to collaborate effectively with various teams across the organization. The successful candidate will be at the forefront of machine learning innovation, contributing to the development of systems that have a direct impact on Affirm's mission to make credit more honest and friendly.

What You Will Do

• Lead the development of new fraud prediction models using a combination of approaches for tabular, graph, and behavioral data.
• Build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams as necessary.
• Prototype new modeling ideas and features, conduct offline experiments, and drive the implementation of the best-performing approaches into production with appropriate risk controls.
• Productionize models by integrating them into batch and/or real-time decision systems and improve their reliability, latency, and operational robustness.
• Instrument and monitor model and data health, helping to define retraining/backtesting workflows as fraud patterns evolve.
• Identify and implement foundational improvements to how the team builds models.
• 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.
• Participate in code reviews, providing feedback to other engineers to maintain high-quality, extensible code.

What We Are Looking For

• 6+ years of experience researching, training, tuning, and launching machine learning models at scale. Relevant PhD can count for up to 2 years of experience.
• A 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 with gradient-boosted decision trees like LightGBM/XGBoost/CatBoost or similar.
• Experience with a deep learning framework, with PyTorch being preferred.
• Experience working with distributed data processing or parallel compute frameworks, with Spark being preferred.
• Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring.
• Proficiency in using AI-powered developer tools to accelerate iteration, debugging, and code quality.
• Mastery in taking a simple problem or business scenario into a solution that interacts with multiple software components, executing on it by writing clear, easily understood, well-tested, and extensible code.
• Comfort navigating a large code base, debugging others' code, and providing feedback through code reviews.

Nice to Have

• Experience with containerization (e.g., Docker) for model serving and deployment.
• Knowledge of cloud-based infrastructure (AWS, GCP, Azure) for deploying and managing ML models.
• Familiarity with agile development methodologies and version control systems like Git.

Benefits and Perks

• Competitive salary and equity package.
• Comprehensive health, dental, and vision insurance.
• Flexible PTO policy and remote work stipend.
• Opportunities for professional growth and development within a dynamic and innovative company.
• Access to cutting-edge technologies and tools.
• Collaboration with a talented team of professionals who are passionate about machine learning and its applications.

• Ensure your resume and cover letter highlight specific examples of machine learning model development and deployment in production environments.
• Familiarize yourself with Affirm's approach to machine learning and fraud detection to demonstrate your interest and understanding of the company's challenges.
• Prepare examples of how you've handled model interpretability and explainability in your previous roles.
• Showcase your proficiency in Python and deep learning frameworks through personal projects or contributions to open-source repositories.
• Be ready to discuss your experience with ML lifecycle tooling and how you've integrated models into real-time decision systems.
• Consider including a link to your GitHub profile or a portfolio of your projects to demonstrate your coding skills and experience with machine learning.