Senior Machine Learning Engineer (Fraud)

Affirm
Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, Saskatchewan
Workplace: RemoteFull timeUSD 153,000 - 213,000 annuallyFunction: Data Science & Machine LearningSkills: ["Communication","Collaboration","Ownership","Code reviews","Debugging"]

Build and improve ML systems on the ML Fraud team that make real-time transaction decisions. Lead development of fraud prediction models using tabular, graph, and behavioral data; create feature pipelines and training datasets; prototype and run offline experiments; and productionize models for batch and real-time systems. Monitor model and data health, define retraining workflows, and collaborate across engineering, fraud analytics, product, and ML platform to deliver measurable impact.

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FursaFursa
Affirm
Affirm
1 month ago

Senior Machine Learning Engineer (Fraud)

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Source: Company careers pageValidated by: Fursa AI
Last checked: 18 hours agoStatus: Live

Job Summary

Build and improve ML systems on the ML Fraud team that make real-time transaction decisions. Lead development of fraud prediction models using tabular, graph, and behavioral data; create feature pipelines and training datasets; prototype and run offline experiments; and productionize models for batch and real-time systems. Monitor model and data health, define retraining workflows, and collaborate across engineering, fraud analytics, product, and ML platform to deliver measurable impact.
Location: Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, Saskatchewan
Workplace: Remote
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Lead development of new fraud prediction models using tabular, graph, and behavioral data.
  • •Build and scale feature pipelines and training datasets from proprietary and third-party signals.
  • •Prototype modeling ideas and features, run offline experiments, and drive best approaches into production with risk controls.
  • •Productionize models by integrating into batch and/or real-time decision systems to improve reliability, latency, and operational robustness.
  • •Instrument and monitor model and data health, define retraining/backtesting workflows, and collaborate across teams to communicate results.

Pay and Benefits

Salary: USD 153,000 - 213,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVisionEquityPaid Leave

Key Requirements

  • •6+ years of experience researching, training, tuning, and launching ML models at scale (relevant PhD can count up to 2 years).
  • •Track record delivering high-impact ML models in a low-latency live setting.
  • •Strong Python skills and experience writing production-quality code.
  • •Experience with tabular classification models, preferably gradient-boosted decision trees (LightGBM/XGBoost/CatBoost or similar).
  • •Experience with deep learning and distributed/parallel compute frameworks (PyTorch preferred; Spark preferred; Ray/Dask or similar).
Experience:FraudMachine learningReal-time systemsLow latencyFintech
Skills:CommunicationCollaborationOwnershipCode reviewsDebugging
Languages:English
Tech Stack:PythonLightGBMXGBoostCatBoostPyTorchSparkRayDaskKubeflowAirflowMLflowClaude CodeCursor

Company Brief

Affirm
Provides point-of-sale lending and buy-now-pay-later (BNPL) solutions for consumers and merchants, enabling installment payments, consumer financing, and embedded financial services through APIs and partnerships with retailers and platforms.
Industry: Lending
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Francisco, United States
Founded: 2012
WebsiteLinkedIn