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This listing was closed on Jul 18, 2026.
Browse Open JobsMachine Learning Engineer II (Servicing ML)
Summary by AIEngineer.careers
Machine Learning Engineer II (Servicing ML) is a Machine Learning Engineer role (full-time). with Affirm. in REMOTE, WORLDWIDE. Compensation shown: $165K–$225K. Imported listing (source: linkedin.com). Apply on the employer's site (linkedin.com).
Imported listing (source: linkedin.com) · Apply on linkedin.com
REMOTE · WORLDWIDE
$165K–$225K
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from linkedin.com
Affirm is building machine learning and AI systems to automate customer operations. The Servicing ML team develops models for disputes, returns, fraud, and chargebacks. This role involves building ML models and LLM-powered pipelines, collaborating with cross-functional teams, and taking projects from idea to production.
Responsibilities
- Develop AI systems that automate dispute and chargeback handling
- Build models that automate refunds
- Build and maintain evidence extraction pipelines using LLM-powered workflows
- Prototype new modeling ideas, run offline experiments, and drive best-performing approaches into production
- Collaborate with Engineering, Servicing Operations, Product, and ML Platform
Requirements
- 2+ years of experience as a machine learning engineer
- Strong Python skills and production-quality code
- Experience with gradient-boosted decision trees (LightGBM/XGBoost/CatBoost)
- Experience with LLM APIs (OpenAI, Anthropic) and orchestration frameworks (LangChain, LangGraph)
- Familiarity with document and unstructured data processing
- Experience with ML lifecycle tooling (Kubeflow, Airflow, MLflow)
Affirm
San Francisco · US · 3131+ employees
Affirm is a fintech company that provides point-of-sale financing solutions, allowing consumers to pay for purchases in installments. It operates a payment network connecting consumers and merchants.
