Machine Learning Engineer II (Underwriting ML)
- Role
- AI / ML
- Experience
- Mid
Open to US only. Set where you work from to check your eligibility.
No BS summary
Machine learning engineer with 2+ years or relevant PhD, focused on underwriting prediction models for real-time credit decisions. Must have strong Python, production-quality code, classification modeling, ML lifecycle tooling, distributed data/parallel compute experience, and AI-powered developer tool usage. Remote US only.
Core skills
Optional skills
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.
What you’ll do
- 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.
What we look for
- 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
Pay Grade - L 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.
Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)
USA base pay range (CA, WA, NY, NJ, CT) per year: $165,000 - $225,000 USA base pay range (all other U.S. states) per year: $146,000 - $206,000
#LI-Remote
What you'll do
- Develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential 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.
- Help productionize models by integrating them into batch and/or real-time decision systems and improving reliability, latency, and operational robustness.
- Instrument and monitor model and data health.
- Help define retraining and backtesting workflows.
- Collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to technical and non-technical audiences.
What they require
- 2+ years of total 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.
- Experience with a deep learning framework.
- Experience working with distributed data processing or parallel compute frameworks.
- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring.
- Proficient in using AI-powered developer tools to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.
- Ability to take a simple problem or business scenario into a solution that interacts with multiple software components and execute 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.
- Takes ownership of growth and proactively seeks feedback from the team, manager, and stakeholders.
- Strong verbal and written communication skills that support effective collaboration with a global engineering team.
- Equivalent practical experience or a Bachelor’s degree in a related field.
Benefits
- Equity rewards may be included in the total compensation package.
- Monthly stipends for health, wellness, and tech spending.
- Benefits including 100% subsidized medical coverage, dental, and vision for you and your dependents.
- Health care coverage with Affirm covering all premiums for all levels of coverage for you and your dependents.
- Flexible Spending Wallets with generous stipends for Technology, Food, various Lifestyle needs, and family forming expenses.
- Competitive vacation and holiday schedules.
- Employee stock purchase plan enabling employees to buy Affirm shares at a discount.
- Inclusive interview experience and reasonable accommodations for candidates who need individualized support.
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without hidden fees or compounding interest.
What people say about this company
3.7/ 5