Senior Machine Learning Engineer, Fraud Risk Modeling

GEICO is seeking a Senior Machine Learning Engineer for Fraud Risk Modeling in Palo Alto, CA (onsite). In this role, you will design, implement, and deploy production-grade machine learning models, build the infrastructure that supports reliable model delivery, and help teams turn model outputs into scalable services that perform in real-world environments.

Benefits and support

• Competitive pay, benefits, and flexibility to support your well-being and future.
• Personalized development programs, mentorship, and certification assistance.

What you’ll do

• Lead model architecture and implementation, partnering with Product, Business Units, and Engineering teams to deliver fraud risk capabilities.
• Build scalable ML infrastructure for model training, automated hyperparameter tuning, and deployment pipelines.
• Develop production-grade ML services and APIs, writing modular, maintainable code that supports reuse in future ML projects.
• Troubleshoot and optimize model performance by tracking key metrics and improving reliability, speed, and efficiency in production.
• Own end-to-end model lifecycle management, including monitoring, retraining, and version management to keep models aligned with business needs over time.
• Provide leadership and mentorship to junior machine learning engineers and promote best practices across software engineering, model development, and deployment.
• Collaborate cross-functionally with data engineering, software development, and product management to ensure smooth integration and operations in production systems.
• Stay current with industry trends by evaluating and incorporating new machine learning techniques and system engineering tools.

What you bring

• B.Sc. in Computer Science, Machine Learning, Engineering, or a related technical field.
• 6+ years of hands-on experience applying machine learning in production, including deep learning, reinforcement learning, and NLP.
• 6+ years experience using open-source and cloud-agnostic components such as:
  • Data warehouses (example: Snowflake)
  • Streaming platforms (example: Kafka)
  • Relational databases (example: PostgreSQL)
  • NoSQL (example: MongoDB, Cassandra)
  • Distributed processing (example: Spark, Ray)
  • Workflow management (example: Airflow, Temporal)
• 6+ years of professional software development with at least two general-purpose languages such as Java, C++, Python, or C#.
• 6+ years with machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn.
• At least 4 years working with cloud platforms (AWS, Azure, GCP) and containerization (Docker), plus orchestration with Kubernetes.
• Proven production deployment experience for machine learning models with scalability, reliability, and high availability.
• Strong engineering foundations including object-oriented design, design patterns, clean and maintainable code, Git proficiency, and familiarity with Agile methodologies.
• Distributed systems knowledge for scaling ML workloads, including distributed data processing and microservices architectures.
• MLOps experience implementing CI/CD, automated testing, and deployment pipelines for ML models.
• System architecture and performance optimization, including fault-tolerant designs for large-scale data and high-volume requests.
• Cloud-based ML design and deployment (AWS, Azure, Google Cloud) and familiarity with tools such as AWS SageMaker, GCP AI Platform, or Azure Machine Learning.
• Monitoring and logging experience to track performance and support efficient resource utilization.

Tools you’ll work with

• Java, C++, Python, C#
• TensorFlow, PyTorch, Scikit-learn
• AWS, Azure, GCP
• Docker, Kubernetes
• Snowflake, Kafka, PostgreSQL, MongoDB, Cassandra
• Spark, Ray, Airflow, Temporal
• Git, AWS SageMaker, GCP AI Platform, Azure Machine Learning

Preferred qualifications

• Experience designing and building high-performance distributed systems for large-scale data ingestion and processing for ML workloads.
• Experience with real-time inference pipelines and low-latency model serving.
• Familiarity with serverless computing or managed services for ML deployment.
• Advanced degree (M.Sc., Ph.D.) in a related field.
• Experience optimizing model training and inference for GPU/TPU acceleration.

Compensation

Annual salary: $115,000.00 - $230,000.00

The GEICO pledge

• Great Company: Protecting customers through life’s twists and turns with innovation and integrity.
• Great Careers: Personalized development programs, mentorship, and certification assistance.
• Great Culture: Inclusive and collaborative culture rooted in shared success.
• Great Rewards: Competitive pay, benefits, and flexibility to support your well-being and future.