Machine Learning Engineer II (Fintech)

TL;DR

Machine Learning Engineer II (Fintech): Building and improving ML and AI systems to automate customer operations such as disputes, returns, and fraud with an accent on LLM-powered workflows and tabular classification. Focus on developing evidence extraction pipelines from unstructured data and optimizing automated refund and chargeback systems.

Location: Remote (US)

Salary: $142,000 – $210,000 per year

Company

hirify.global is reinventing credit to make it more honest and friendly, providing "buy now and pay later" flexibility without hidden fees or compounding interest.

What you will do

• Develop AI systems to automate dispute and chargeback handling using structured evidence and business logic.
• Build models to automate refunds, ensuring faster reimbursement for customers.
• Maintain evidence extraction pipelines using LLM-powered workflows to process unstructured data.
• Prototype new modeling ideas, run offline experiments, and drive high-performing approaches into production.
• Collaborate with Engineering, Product, and ML Platform teams to define requirements and evaluate tradeoffs.

Requirements

• 2+ years of experience as a machine learning engineer.
• Experience with tabular classification problems, preferably using LightGBM, XGBoost, or CatBoost.
• Experience building applications with LLM APIs (OpenAI, Anthropic) and orchestration frameworks like LangChain or LangGraph.
• Proficiency in Python and experience writing production-quality, well-tested code.
• Familiarity with ML lifecycle tooling such as Kubeflow, Airflow, or MLflow.
• Must be based in the United States.

Culture & Benefits

• Remote-first company culture with flexible work arrangements within the country of employment.
• 100% subsidized medical, dental, and vision coverage for employees and their dependents.
• Generous Flexible Spending Wallets for technology, food, and lifestyle needs.
• Competitive vacation and holiday schedules.
• Employee Stock Purchase Plan (ESPP) to buy company shares at a discount.