{"absolute_url":"https://job-boards.greenhouse.io/affirm/jobs/7806920003","data_compliance":[{"type":"gdpr","requires_consent":false,"requires_processing_consent":false,"requires_retention_consent":false,"retention_period":null,"demographic_data_consent_applies":false}],"internal_job_id":5796398003,"location":{"name":"Remote Canada"},"metadata":[{"id":4128552003,"name":"External Department","value":"Engineering","value_type":"single_select"},{"id":28890347003,"name":"PERM Job?","value":false,"value_type":"yes_no"}],"id":7806920003,"updated_at":"2026-08-20T17:01:53-04:00","requisition_id":"JR104061","title":"Senior Machine Learning Engineer (Fraud)","company_name":"Affirm","first_published":"2026-07-21T11:10:34-04:00","language":"en","application_deadline":null,"content":"\u0026lt;div class=\u0026quot;content-intro\u0026quot;\u0026gt;\u0026lt;p\u0026gt;At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most.\u0026lt;/p\u0026gt;\u0026lt;/div\u0026gt;\u0026lt;div\u0026gt;\n\u0026lt;p data-pm-slice=\u0026quot;1 1 []\u0026quot;\u0026gt;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.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;br\u0026gt;\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;What you’ll do\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness.\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- Identify and implement foundational improvements to how the team builds models.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- 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.\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;What we look for\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- 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.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- Track record of delivering high impact machine learning models in a low latency live setting\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- Strong Python skills and experience writing production-quality code.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar).\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- Experience with a deep learning framework (PyTorch preferred).\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask or similar).\u0026amp;nbsp;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- 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.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- 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.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- You are comfortable navigating a large code base, debugging others\u0026#39; code, and providing feedback to other engineers through code reviews.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;- You have strong verbal and written communication skills that support effective collaboration with our global engineering team.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;Pay Grade - N\u0026lt;br\u0026gt;Equity Grade - 6\u0026lt;br\u0026gt;\u0026lt;br\u0026gt;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.\u0026amp;nbsp;\u0026lt;br\u0026gt;\u0026lt;br\u0026gt;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).\u0026lt;br\u0026gt;\u0026lt;br\u0026gt;CAN base pay range per year: $153,000 - $213,000\u0026lt;br\u0026gt;\u0026lt;br\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Location - Remote Canada\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;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.\u0026amp;nbsp;\u0026lt;/strong\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;/div\u0026gt;\n\u0026lt;div\u0026gt;\u0026lt;strong\u0026gt;\u0026lt;span style=\u0026quot;font-weight: 400;\u0026quot;\u0026gt;#LI-Remote\u0026lt;/span\u0026gt;\u0026lt;/strong\u0026gt;\u0026lt;/div\u0026gt;\u0026lt;div class=\u0026quot;content-conclusion\u0026quot;\u0026gt;\u0026lt;div\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Remote-first with flexibility built in\u0026lt;/strong\u0026gt;\u0026lt;strong\u0026gt;\u0026lt;br\u0026gt;\u0026lt;/strong\u0026gt;Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;strong\u0026gt;Benefits designed for you\u0026lt;/strong\u0026gt;\u0026lt;strong\u0026gt;\u0026lt;br\u0026gt;\u0026lt;/strong\u0026gt;Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights:\u0026lt;/p\u0026gt;\n\u0026lt;ul\u0026gt;\n\u0026lt;li\u0026gt;Health coverage at no cost: We cover 100% of premiums for employees and their dependents.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to.\u0026lt;/li\u0026gt;\n\u0026lt;li\u0026gt;Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\u0026lt;p\u0026gt;We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law.\u0026lt;/p\u0026gt;\n\u0026lt;/div\u0026gt;\n\u0026lt;div\u0026gt;\n\u0026lt;p\u0026gt;By clicking \u0026quot;Submit Application,\u0026quot; you acknowledge that you have read Affirm\u0026#39;s\u0026amp;nbsp;\u0026lt;a href=\u0026quot;https://www.affirm.com/global-candidate-privacy-notice\u0026quot; target=\u0026quot;_blank\u0026quot;\u0026gt;Global Candidate Privacy Notice\u0026lt;/a\u0026gt; and consent to the use of your personal information as described.\u0026lt;/p\u0026gt;\n\u0026lt;/div\u0026gt;\u0026lt;/div\u0026gt;","departments":[{"id":4057215003,"name":"Checkout","child_ids":[],"parent_id":4005044003}],"offices":[{"id":4022513003,"name":"Remote Canada","location":null,"child_ids":[],"parent_id":4025301003}],"ai_disclaimer":null,"include_ai_disclaimer":false,"ai_opt_out_request_url":null}