Senior Machine Learning Engineer (Fraud)

Affirm; United States; Remote; USD 150,000 - 200,000; Full time

Benefits offered by this job

Health care coverage

Flexible Spending Wallets

Competitive vacation and holiday schedules

Employee stock purchase plan

Job summary

Affirm is seeking a Senior Machine Learning Engineer (Fraud) to lead the development of fraud prediction models. You will be working remotely and will collaborate with cross-functional teams to build ML systems for real-time transaction decisions. Candidates should have 6+ years of experience in machine learning, strong Python skills, and familiarity with relevant models and tools. Affirm offers a competitive salary range of $150,000 - $200,000, along with comprehensive benefits including health coverage and employee stock purchase plans.

Qualifications

• 6+ years experience with machine learning models.
• Strong Python skills and production-quality coding.
• Experience with model evaluation for low latency settings.

Responsibilities

• Lead development of new fraud prediction models.
• Scale feature pipelines and training datasets.
• Integrate models into decision systems.

Skills

Machine learning

Python

Deep learning frameworks (PyTorch preferred)

Distributed data processing (Spark preferred)

Model monitoring and orchestration tools

Education

PhD in a relevant field

Tools

LightGBM

XGBoost

Kubeflow

Airflow

MLflow

Job description

Senior Machine Learning Engineer (Fraud)

Remote Canada

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.

What you’ll do

• Lead development of new fraud prediction models using a mix of 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: integrate into batch and/or real‑time decision systems, and improve reliability, latency, and operational robustness.
• 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.
• 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

• 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.
• 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.
• Comfortable navigating a large code base, debugging others’ code, and providing feedback to other engineers through code reviews.
• Experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.
• Strong verbal and written communication skills that support effective collaboration with our global engineering team.

Location - Remote Canada

Base pay range: $150,000 - $200,000 per year. Compensation may include equity rewards and monthly stipends for health, wellness and technology spending, and benefits such as 100% subsidized medical coverage, dental, and vision for you and your dependents.

Benefits

• Health care coverage – covers all premiums for all levels of coverage for you and your dependents.
• Flexible Spending Wallets – generous stipends for technology, food, lifestyle needs, and family formation 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.

Affirm will consider qualified applicants with arrest and conviction records pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance.

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.