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HomeCompaniesAffirmManager, Machine Learning Engineering (Fraud)

Manager, Machine Learning Engineering (Fraud)

Affirm · Remote Canada · Remote · Active · $178,000–$228,000 / year · Greenhouse

Job facts

FieldValue
CompanyAffirm
TitleManager, Machine Learning Engineering (Fraud)
Normalized title-
Department / teamCheckout
LocationCanada
Work modelRemote / Remote
Employment type-
Salary$178,000–$228,000 / year
Statusactive
ATS providerGreenhouse
Posted / first seen2026-04-23 / 2026-05-29
Changed / last seen2026-05-29 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Affirm.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through Greenhouse.Open
Provider filtered searchThe same provider as a filtered job collection.Open
Department jobsActive postings in Checkout.Open
Work model jobsActive Remote postings.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyAffirm
Sourced75e74c2-8678-44e5-952f-d8f3f2802a53
ATS providerGreenhouse

Description

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. The Fraud Machine Learning team builds models that power critical decisions during the loan application process, protecting Affirm and its customers from fraud while maintaining a seamless user experience. As a manager, you will lead a team of ML engineers developing and improving models that detect and prevent abuse in a rapidly evolving, adversarial environment. In this role, you will define the technical and modeling strategy for fraud detection, guiding the team across the full machine learning lifecycle from feature development and experimentation to production deployment and monitoring. You will partner closely with Product, Fraud Analytics, Risk, and Platform teams to ensure high-quality models are effectively integrated into decisioning systems. You will also help drive the evolution of modeling approaches at Affirm, including the adoption of representation learning and transformer-based techniques to better capture complex behavioral patterns. What you’ll do Set the technical and modeling strategy for fraud detection, aligning team efforts with key business outcomes such as fraud loss reduction, approval rates, and customer experience Lead a team of machine learning engineers to design, build, and iterate on high-impact fraud models across the full ML lifecycle, from experimentation to production Drive the evolution of modeling approaches, including the adoption of representation learning, transformer-based methods, and other advanced techniques for modeling complex behavioral data Partner cross-functionally with Product, Fraud Analytics, Risk, and Engineering to define solutions, evaluate trade-offs, and ensure models are effectively integrated into decisioning systems Develop talent by coaching engineers, providing feedback, and fostering a high-performing team culture grounded in technical excellence and ownership What we look for Bachelor’s in a technical field with 8+ years of industry experience, including 3+ years managing engineers Experience with modern ML approaches, including representation learning, deep learning, or transformer-based models, as well as traditional methods such as gradient-boosted trees Proven ability to lead teams delivering end-to-end ML solutions in production environments, including experimentation, evaluation, and model iteration in production Strong engineering fundamentals and experience working with scalable systems and data pipelines Track record of effective cross-functional collaboration with product, analytics, and engineering partners Ability to operate in ambiguous, fast-evolving environments and drive clarity, prioritization, and execution This position requires either equivalent practical experience or a Bachelor’s degree in a related field. Pay Grade - P Equity Grade - 7 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: $178,000 - $228,000 #LI - Remote Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities. We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include: 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 We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process. [For U.S. positions that could be performed in Los Angeles or San Francisco] Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records. By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.

Full job record

Job ID862d9ce6d9f854c025236f2fe1bc9f6eb0f99a95
Org IDd0e2d504-3368-4b33-b253-c085fb0af06e
Source IDd75e74c2-8678-44e5-952f-d8f3f2802a53
Board IDd75e74c2-8678-44e5-952f-d8f3f2802a53
Providergreenhouse
Provider Job Key7710180003
TitleManager, Machine Learning Engineering (Fraud)
Normalized Title
Statusactive
Activeyes
Location TextRemote Canada
DepartmentCheckout
Team
Employment Type
Workplace Typeremote
Remote Policyremote
CountryCanada
Region
City
Salary Rawbase pay range per year: $178,000 - $228,000 #LI - Remote Affirm is proud to be a remote-first company
Salary Min178,000
Salary Max228,000
Salary CurrencyUSD
Salary Periodyear
Source URLhttps://job-boards.greenhouse.io/affirm/jobs/7710180003
Apply URLhttps://job-boards.greenhouse.io/affirm/jobs/7710180003
First Seen At2026-05-29 22:43:02Z
Last Seen At2026-06-06 07:35:17Z
Last Checked At2026-06-06 07:35:17Z
Last Changed At2026-05-29 22:43:02Z
Inactive At
Source Posted At2026-04-23 18:43:01Z
Source Updated At2026-05-07 15:22:55Z
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=greenhouse/board=affirm/date=2026-06-06/2026-06-06T07-35-16-782Z-7407a32524bfdc26922d7404954e7cc29daba13af1a46009b6fed215b9387706.json
Event Fields
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  "last_changed_at": "2026-05-29T22:43:02.097Z",
  "active_status": "active"
}
Parsed Structured
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  "inferred_at": "2026-06-06T07:35:17.148Z",
  "launch_scope": {
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  "remote_policy": "remote",
  "salary_period": "year",
  "workplace_type": "remote",
  "salary_currency": "USD"
}
Extensions
{}
Native Structured
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  "company_name": "Affirm",
  "requisition_id": 5754390003,
  "first_published": "2026-04-23T14:43:01-04:00",
  "application_deadline": null
}
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