Senior Machine Learning Engineer, Fraud Risk Modeling

Why Join GEICO?

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 on relentless innovation to exceed our customers' expectations while making a real impact on local communities nationwide.

Founded in 1936, GEICO is a member of the Berkshire Hathaway family of companies and one of the largest auto insurers in the United States. 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.

GEICO is seeking an experienced Senior Machine Learning Engineer to join the AI organization. This person will take on a critical leadership role in designing, implementing, and deploying cutting-edge machine learning models that solve real-world business challenges. You will collaborate with various business units to build scalable, high-performance ML systems with a strong emphasis on system design. In addition to technical contributions, you will mentor junior engineers, drive the full lifecycle of machine learning model development, and ensure that models are seamlessly integrated into production. This position requires expertise in both machine learning and software engineering to develop robust, production-grade solutions.

Key Responsibilities:

• Lead the Design & Implementation of ML Models: Lead the architecture and implementation of machine learning models, working closely with Product, Business Units, and Engineering teams.
• Build Scalable Infrastructure: Design and develop scalable infrastructure for model training, automated hyperparameter tuning, and deployment pipelines, ensuring that systems are reliable and performant at scale.
• Write Production-Grade Code for ML Services and APIs: Write high-quality, maintainable production-grade code that turns machine learning models into deployable services and APIs. Ensure that code is modular and reusable for future ML projects.
• Optimize Model Performance and Resolve Issues: Debug and troubleshoot model performance issues, track key metrics, and continuously enhance model reliability, speed, and efficiency in production environments.
• End-to-End Model Lifecycle Management: Own the complete lifecycle of ML models, including monitoring, retraining, and managing versions of models to ensure they continue to meet business needs over time.
• Leadership and Mentorship: Guide and mentor junior machine learning engineers, promote best practices in software engineering, model development, and deployment. Lead technical decision-making processes and foster collaboration within the team.
• Collaboration Across Teams: Collaborate with cross-functional teams (e.g., data engineering, software development, and product management) to integrate machine learning models and ensure smooth deployment and operations in production systems.
• Stay Up to Date with Industry Trends: Continuously explore and integrate new machine learning techniques and system engineering tools, ensuring the team remains at the forefront of machine learning and systems architecture practices.

Basic Qualifications:

• B.Sc. in Computer Science, Machine Learning, Engineering, or a related technical field.
• 6+ years of hands-on experience applying machine learning techniques, including deep learning, reinforcement learning, and NLP in production environments.
• 6+ years of experience utilizing open-source/cloud-agnostic components such as data warehouse (e.g. snowflake), streaming platform (e.g. Kafka), relational database (e.g. PostgreSQL), NoSQL (e.g. MongoDB, Cassandra), distributed processing (e.g. Spark, Ray), workflow management (e.g. Airflow, Temporal), etc.
• 6+ years of professional software development experience with at least two general-purpose programming languages such as Java, C++, Python or C#.
• 6+ years of experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn for model development.
• At least 4 years of experience with cloud platforms (AWS, Azure, GCP) and containerization technologies such as Docker, as well as orchestration tools like Kubernetes.
• Proven experience in deploying machine learning models in a production environment, ensuring scalability, reliability, and high availability.

Core Engineering Skills & Knowledge:

• Extensive experience with object-oriented design (OOD), design patterns, writing clean, and maintainable code. Proficiency in version control (Git) and familiarity with Agile methodologies.
• Solid understanding of distributed systems and the challenges associated with scaling machine learning models in production, such as managing distributed data processing and microservices architectures.
• Expertise in implementing MLOPs practices, including setting up continuous integration (CI), continuous delivery (CD), automated testing, and deployment pipelines for ML models.
• Strong understanding of system architecture, performance optimization, designing fault-tolerant systems that handle large-scale data and high-volume requests.
• Experience designing and deploying machine learning models using cloud-based environments like AWS, Azure, or Google Cloud. Familiarity with cloud-native tools such as AWS Sage Maker, GCP AI Platform, or Azure Machine Learning.
• Experience setting up monitoring and logging systems to track performance in production environments and ensuring efficient resource utilization.

Preferred Qualifications:

• Experience with designing and building high-performance distributed systems that handle large-scale data ingestion and processing for machine learning workloads.
• Experience with real-time inference pipelines and low-latency model serving.
• Familiar with serverless computing or managed services for ML model deployment.
• Advanced degree (M.Sc., Ph.D.) in a related field is a plus.
• Experience in working with GPU/TPU optimization for accelerated model training and inference.

Annual Salary

The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate’s work experience, education and training, the work location as well as market and business considerations.

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.

The equal employment opportunity policy of the GEICO Companies provides for a fair and equal employment opportunity for all associates and job applicants regardless of race, color, religious creed, national origin, ancestry, age, gender, pregnancy, sexual orientation, gender identity, marital status, familial status, disability or genetic information, in compliance with applicable federal, state and local law. GEICO hires and promotes individuals solely on the basis of their qualifications for the job to be filled.

GEICO reasonably accommodates qualified individuals with disabilities to enable them to receive equal employment opportunity and/or perform the essential functions of the job, unless the accommodation would impose an undue hardship to the Company. This applies to all applicants and associates. GEICO also provides a work environment in which each associate is able to be productive and work to the best of their ability. We do not condone or tolerate an atmosphere of intimidation or harassment. We expect and require the cooperation of all associates in maintaining an atmosphere free from discrimination and harassment with mutual respect by and for all associates and applicants.