Machine Learning Engineer II (Servicing ML

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 Servicing ML team, you will build and improve machine learning and AI systems that automate customer operations such as disputes, returns, fraud, and chargebacks to make the best decisions for affirm and our customers. You will 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.

• You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.
• You will build models that automate refunds, getting money back to our customers faster.
• You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.
• You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
• You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non‑technical audiences.

• You have a total of 2+ years of experience as a machine learning engineer
• 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/Cat Boost).
• Experience building applications with LLM APIs (e.g., OpenAI, Anthropic), including structured extraction, prompt engineering, and orchestration frameworks like Lang Chain or Lang Graph.
• Familiarity with document and unstructured data processing (PDF/image extraction, text parsing, 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

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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

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 remote, but will occasionally be required to work out of their assigned affirm office. A limited number of roles remain office‑based due to the nature of their job responsibilities.

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