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# Machine Learning Engineer I (MLE I), Fraud Risk Modeling

- **Company:** GEICO
- **Location:** Palo Alto, CA, United States
- **Experience:** Starter
- **Salary:** $90,000.0 - $185,000.0
- **Contract:** Internship / Graduate position
- **Skills:** Clean Code Principles, Java (Programming Language), Amazon Web Services, Microsoft Azure, Big Data, Code Review, Data Validation, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Feature Engineering, Pytorch, Snowflake, Apache Spark, Model Validation, Reliability of Systems, Containerization, Semi-structured Data, Scikit Learn, Information Technology, Machine Learning Operations, Data Pipelines
- **Published:** October 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a2dd387b27ead207

## About the Role

* Bachelor's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related field.
 * 1-3 years of hands-on experience building or supporting ML models or data pipelines (internships count).
 * Proficiency in Python and familiarity with ML libraries (e.g., scikit-learn, PyTorch, TensorFlow).
 * Basic understanding of ML model training, evaluation, and deployment concepts.
 * Strong problem-solving skills and eagerness to learn production ML systems.

Preferred Qualifications

 * Exposure to fraud detection, risk modeling, or anomaly detection problems.
 * Familiarity with SQL and large-scale data platforms (e.g., Spark, Snowflake).
 * Experience with cloud platforms (AWS or Azure) or containerized environments.
 * Interest in responsible AI, model monitoring, and explainability.

## Description

Machine Learning Development & Implementation

 * Implement, test, and maintain machine learning models used in fraud detection and risk assessment.
 * Support feature engineering, data validation, and model experimentation under guidance from senior engineers.
 * Assist in deploying models into production using established MLOps pipelines and tooling.

Data & Pipeline Contributions

 * Work with structured and semi-structured data to build reliable training and inference datasets.
 * Contribute to batch and near-real-time ML pipelines for fraud scoring and decisioning.
 * Help monitor data quality, model performance, and system reliability.

Engineering Best Practices

 * Write clean, well-tested, maintainable code in Python (and/or Java).
 * Participate in code reviews, design discussions, and post-incident reviews.
 * Follow security, compliance, and governance standards required in a regulated environment.

Collaboration & Learning

 * Collaborate closely with data scientists, product managers, and senior ML engineers.
 * Learn industry best practices for model lifecycle management, monitoring, and experimentation.
 * Actively seek feedback and continuously improve technical and domain knowledge.