Senior Machine Learning Engineer (Fraud)

Навыки

• Airflow
• XGBoost / LightGBM / CatBoost
• Коммуникация
• Отладка и поиск ошибок
• Machine Learning
• MLOps
• Ответственность за результат

О компании и продукте

• 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.
• 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.

Задачи

• 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

Требования

• 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
• In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company)
• 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
• 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

Условия

• 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)
• CAN base pay range per year: $153,000 - $213,000

Паспорт вакансии

История публикации

Откуда что взялось

Отмечено то, что вывели мы. Без пометки — значение назвал работодатель.

Почему на этом месте в выдаче

Порядок выдачи объявлен контрактом: свежесть решает между днями, полнота и зарплата — внутри дня.