Machine Learning Engineer II (Servicing ML)
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Job description
About the Position
Machine Learning Engineer II (Servicing ML)
Remote US
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.
Responsibilities
- Develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.
- Build models that automate refunds, getting money back to our customers faster.
- Build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.
- Prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
- 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.
Requirements
- 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/CatBoost).
- Experience building applications with LLM APIs (e.g., OpenAI, Anthropic), including structured extraction, prompt engineering, and orchestration frameworks like LangChain or LangGraph.
- 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.
- Mastery in 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 demonstrating 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.
- Bachelor’s degree in a related field or equivalent practical experience.
We Offer
- Base Pay Grade: L
- Equity Grade: 6
- Base Pay Range: $165,000 - $225,000 per year (CA, WA, NY, NJ, CT); $146,000 - $206,000 per year (all other U.S. states)
- Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents
- Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming 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
About the Company
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. A limited number of roles remain office-based due to the nature of their job responsibilities.
We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:
- Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents
- Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming 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.
[For U.S. positions that could be performed in Los Angeles or San Francisco] Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records.
By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.
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Financial Services
About the Company Affirm’s mission is to deliver honest financial products that improve lives. The company aims to build a new kind of payment network based on trust, transparency, and prioritizing people. This approach empowers millions of consumers to spend and save responsibly. Products, Services & Tech Stack Core Solutions: Financial products designed to improve consumer lives through responsible spending and saving. Project Scope & Target Clients Client Base: The specific target verticals or client types served are not mentioned in the provided text. Engineering Scale: The scope and complexity of engineering projects are not mentioned in the provided text.
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