Machine Learning Engineer II, Fraud Risk Modeling

GEICO • Palo Alto, CA

22h • $105,000 - $215,000

About The Position

At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities. Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers’ expectations while making a real impact for our company through our shared purpose. When you join our company, we want you to feel valued, supported and proud to work here. That’s why we offer The GEICO Pledge: Great Company, Great Culture, Great Rewards and Great Careers. Overview: GEICO’s Fraud Risk Modeling team is building a centralized, multi-modal fraud defense ecosystem that unifies claims, payment, and identity risk assessment. As a Machine Learning Engineer II, you will take ownership of critical ML components, deliver production-grade models, ensure our fraud detection capabilities are robust and high-performing, and help evolve our fraud platform at scale. This role is ideal for engineers who are comfortable operating independently, making technical tradeoffs, and delivering reliable ML systems in production.

Requirements

• Bachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related field.
• 3+ years of hands-on experience building, deploying, and operating ML systems in production.
• Strong proficiency in Python (and/or Java) with a focus on production-quality code.
• Experience with end-to-end ML lifecycle management, including monitoring and retraining.
• Familiarity with distributed systems, data pipelines, and cloud-based ML infrastructure.

Nice To Haves

• Experience in Fraud Detection, Risk Modeling, Trust & Safety, or Identity systems.
• Hands-on experience with big-data technologies (Spark, Snowflake, Redis, Kubernetes).
• Familiarity with workflow orchestration tools like Airflow.
• Exposure to feature stores, real-time ML serving, or graph-based modeling.
• Familiarity with model explainability, fairness, or governance in regulated environments.

Responsibilities

• ML System Ownership Design, implement, and maintain production ML models and features for fraud risk assessment.
• Own model components across the full lifecycle: from data preparation and feature engineering to deployment and monitoring.
• Improve model accuracy, stability, and interpretability through experimentation and iteration.
• Platform & Pipeline Engineering Build and enhance scalable ML pipelines supporting batch and real-time fraud decisioning.
• Build monitoring and alerting to detect data drift, model degradation, and system failures.
• Optimize performance and reliability of ML services under production traffic.
• Technical Collaboration & Mentorship Collaborate with Senior and Staff engineers on system design and architecture decisions.
• Write high-quality, well-tested code and contribute to shared ML libraries and tooling.
• Provide guidance and code reviews for junior engineers (MLE I).
• Translate business and fraud domain requirements into technical ML solutions.
• Operational Excellence Participate in on-call rotations or incident response related to ML services.
• Ensure ML systems meet security, privacy, and regulatory requirements.
• Contribute to technical documentation and best-practice sharing across teams.

Benefits

• Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family’s overall well-being.
• Financial benefits including market-competitive compensation; a 401K savings plan vested from day one that offers a 6% match; performance and recognition-based incentives; and tuition assistance.
• Access to additional benefits like mental healthcare as well as fertility and adoption assistance.
• Supports flexibility- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level