Senior Machine Learning Engineer (Fraud ML)

AffirmAffirm·Remote(Remote Canada)
AI & Machine Learning

WFA Digital Insight

As demand for digital payment solutions surges, the need for skilled machine learning engineers to combat fraud is on the rise. With a growth rate of 27% in the last year, professionals with expertise in machine learning and fraud detection are in high demand. Affirm, a leader in the fintech space, is looking for a Senior Machine Learning Engineer to join their team. This role stands out for its focus on applying machine learning to real-world problems and its collaborative environment. Before applying, candidates should be aware that a strong background in Python, machine learning frameworks, and experience with large datasets is required.

Job Description

About the Role

The Senior Machine Learning Engineer position at Affirm is a key role in the company's efforts to combat fraud and protect its customers. As a member of the ML Fraud team, you will be responsible for developing and improving machine learning systems that make real-time transaction decisions. This role is critical to the company's success, as it requires a deep understanding of machine learning algorithms, data analysis, and software development.

The ideal candidate will have a strong background in machine learning and software development, as well as experience working with large datasets. You will be working closely with cross-functional teams, including data scientists, engineers, and product managers, to develop and deploy machine learning models. Your ability to communicate complex technical concepts to non-technical stakeholders will be essential in this role.

As a Senior Machine Learning Engineer at Affirm, you will be part of a talented team of engineers and data scientists who are passionate about using machine learning to solve real-world problems. You will have the opportunity to work on challenging projects, collaborate with experienced professionals, and contribute to the company's mission of making credit more honest and friendly.

What You Will Do

  • Lead the development of new fraud prediction models using a mix 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 when needed
  • Prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls
  • Productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness
  • 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
  • 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
  • Develop and maintain technical documentation of models and systems
  • Participate in code reviews and contribute to the improvement of the codebase

What We Are Looking For

  • 6+ years of experience researching, training, tuning, and launching ML models at scale
  • 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 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
  • 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

Nice to Have

  • Experience with cloud-based machine learning platforms (e.g., AWS SageMaker, Google Cloud AI Platform, or similar)
  • Familiarity with containerization (e.g., Docker) and orchestration (e.g., Kubernetes)
  • Experience with cybersecurity and data protection best practices
  • Knowledge of regulatory requirements and compliance in the fintech industry

Benefits and Perks

  • Competitive salary and equity package
  • Comprehensive health, dental, and vision insurance
  • Flexible PTO policy and paid holidays
  • Remote work stipend and equipment allowance
  • Access to professional development opportunities and training programs
  • Collaborative and dynamic work environment with a talented team of professionals
  • Opportunity to work on challenging projects and contribute to the company's mission
  • Recognition and rewards for outstanding performance and contributions

How to Stand Out

  • Make sure your resume and cover letter are tailored to the specific requirements of the job posting, highlighting your experience with machine learning and software development.
  • Be prepared to explain complex technical concepts in simple terms during the interview process.
  • Show examples of your previous work and projects, demonstrating your ability to apply machine learning to real-world problems.
  • Research the company and its products, and be ready to discuss how your skills and experience align with the company's mission and goals.
  • Practice your coding skills and be prepared to complete a coding challenge or whiteboarding exercise during the interview.
  • Ask questions during the interview, such as what a typical day looks like in the role, or what opportunities there are for professional development and growth.
  • Be honest and transparent about your experience and qualifications, and be prepared to discuss any gaps or areas for improvement.

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