Senior Staff ML Engineer

GEICO•Bethesda, MD

2d•$150,000 - $300,000

About The Position

GEICO is on a journey to transform the insurance industry with Artificial Intelligence. The Fraud Risk Modeling team is at the center of this evolution. We are not just building models; we are architecting a centralized multi-modal fraud defense ecosystem that protects millions of customers. As a Senior Staff Machine Learning Engineer, you will be a technical anchor for the Fraud Risk Modeling team. You will partner with other AIML teams in building coherent, real-time fraud platform and solutions that unify claims, payment, and identity risk assessment. This is a high-impact role for a builder who cares about architectural elegance, system reliability, and is able to solve complex, large-scale, cross functional problems. This role requires a minimum of 15 years of relevant experience.

Requirements

Bachelor’s degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related field; an advanced degree (master’s or Ph.D.) is highly desirable.
15+ years of hands-on experience in designing, implementing, and optimizing AIML systems in production environments.
Extensive expertise in architecting large-scale data pipelines, real-time AIML serving architectures, and managing the end-to-end AIML lifecycle.
Proven ability to tackle complex technical challenges, innovate through hands-on experimentation, and set technical standards across teams.
Deep proficiency in programming languages such as Python, Java, or similar, with a strong emphasis on coding excellence.
Experience with backend distributed systems & tools (e.g., Airflow, DBT, Kubernetes) and big-data technologies (e.g., Spark, MongoDB, Snowflake, Neo4j, Redis), familiarity with modern data feature stores.
Significant experience working with cloud platforms (AWS, Azure, etc.) and their machine learning services (e.g., SageMaker, Azure ML, etc.).
Familiarity with frameworks for model interpretability, fairness, and regulatory compliance, ensuring ethical and transparent ML systems.
Proficiency in machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, etc.

Nice To Haves

Domain expertise: prior experience in Fraud Detection, Risk Modeling, Trust and Safety, or Digital Identity.
Advanced ML techniques: experience deploying LLM in production (RAG, fine-tuning) or building Graph Neural Networks for network analysis.
Governance: experience with model governance, explainability, and bias mitigation in a regulated industry like insurance.

Responsibilities

Architect and implement scalable, high-performance machine learning platforms and systems capable of processing large data volumes and supporting real-time decision making and workflows.
Design end-to-end AIML pipelines - from data ingestion and feature engineering to model training, deployment, and continuous monitoring.
Evaluate and integrate cutting-edge AIML frameworks and libraries to maintain a state-of-the-art technology stack.
Act as the tech lead across multiple ML feature teams, setting technical direction and ensuring consistency in design principles and best practices.
Provide hands-on mentorship and guidance during design reviews, code assessments, and performance tuning.
Lead by example in tackling complex technical challenges and driving system-wide architectural improvements.
Experiment with and prototype advanced machine learning algorithms and approaches to enhance system performance, model accuracy, and interpretability.
Stay abreast of the latest research and industry trends, translating these insights into actionable, production-level solutions.
Contribute to internal technical documentation and share knowledge across teams.
Oversee the end-to-end lifecycle of machine learning models, ensuring robust testing, deployment, and ongoing monitoring.
Develop and implement systems for model monitoring, alerting, and automated retraining to maintain peak performance in production.
Ensure adherence to industry standards, security protocols, and regulatory compliance throughout the ML lifecycle.
Work closely with data scientists, software engineers, operations, and product teams to seamlessly integrate ML systems into production environments.
Translate complex technical concepts into actionable insights for both technical and non-technical stakeholders.
Foster a collaborative environment that encourages innovation and the sharing of best practices across teams.