Senior Machine Learning Engineer (Fraud)
- Role
- AI / ML
- Experience
- Senior
Open to CA only. Set where you work from to check your eligibility.
No BS summary
Senior ML engineer with 6+ years building and launching production ML models at scale. Must be strong in Python, low-latency live ML systems, tabular classification, distributed processing, ML lifecycle tooling, and production code. Remote Canada only, limited to listed provinces.
Core skills
Optional skills
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.
Pay Grade - N Equity Grade - 6
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 monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company).
CAN base pay range per year: $153,000 - $213,000
Location - Remote Canada
This remote role is open only to candidates residing in Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, or Saskatchewan.
What you'll do
- Lead development of new fraud prediction models using 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 by integrating them into batch and/or real-time decision systems and improving reliability, latency, and operational robustness.
- Instrument and monitor model and data health.
- Help define retraining and 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 to technical and non-technical audiences.
What they require
- 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.
- Experience with a deep learning framework.
- Experience working with distributed data processing or parallel compute frameworks.
- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring.
- Proficient in using AI-powered developer tools to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.
- Ability to turn a simple problem or business scenario into a solution that interacts with multiple software components, writing clear, easily understood, well-tested, and extensible code.
- Comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.
- Takes ownership of growth and proactively seeks feedback from the team, manager, and stakeholders.
- Strong verbal and written communication skills for collaboration with a global engineering team.
- Must reside in Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, or Saskatchewan.
Benefits
- Monthly stipends for health, wellness, and tech spending.
- 100% subsidized medical coverage.
- Dental and vision coverage for you and your dependents.
- May be eligible for equity rewards offered by Affirm Holdings, Inc.
- 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.
- Competitive vacation and holiday schedules.
- Employee stock purchase plan enabling you to buy shares of Affirm at a discount.
- Reasonable accommodations for candidates needing individualized support during the hiring process.
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What people say about this company
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