Machine Learning Engineer

Who you are

• You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field
• Strong Python skills and experience writing production-quality code
• Experience building and evaluating models for 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
• This position requires either equivalent practical experience or a Bachelor’s degree in a related field

What the job involves

• On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout
• 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 user behavior and macroeconomic conditions evolve
• You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential 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 will help 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
• You will collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences

Benefits

• Compensation: We have a simple, flexible, and transparent remote-first compensation structure so you can make the best decisions for yourself and your family
• Spending Wallets: Access tech, food, lifestyle, and family planning wallets for your expenses
• Supportive Communities: Get involved with our employee resource groups and community groups
• Remote-first Workforce: If your role is remote, you can set up shop anywhere in your home country
• Generous Time Off: Take the time you need when life happens
• Health Benefits: Get a plan that fits your needs
• Mental Healthcare: Take care of your mind with great mental health programs
• Parental Leave: Birth and non-birth parents get 18 weeks’ paid leave. Plus, a 4-week return-to-work transition program, at full base pay
• Away Days: We offer 20 company-wide paid days off—which help our teams collectively pause to recharge
• Learning & Development: Engage in exciting learning programs to level up your growth