Senior Machine Learning Engineer Document Intelligence

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.

As a Senior Machine Learning Engineer, you will be a tech lead through design, development, and deployment of advanced machine learning models across the business. This role focuses on building scalable machine learning models, mentoring junior engineers, and driving the full lifecycle of machine learning model development. You will be the tech lead for a team of Machine Learning Engineers and/or Data Scientists focused on ensuring that machine learning models are robust, high-performing, and seamlessly integrated into production systems.

This position involves both hands‑on engineering work and leadership responsibilities in a dynamic environment.

• Design and implement machine learning models & components that solve real‑world business problems in close collaboration with the Product, Business units & Data Science teams.
• Write production‑grade code for ML models as services and APIs.
• Collaborate with cross‑functional teams, including data engineering and software development, to integrate machine learning models into production systems.
• Build and maintain scalable data processing workflows and model deployment infrastructure.
• Debug and resolve model performance issues, track relevant metrics, and implement continuous improvements to ensure model accuracy and reliability.
• Keep up with the latest ML tooling and communities.
• Lead the design and implementation of complex machine learning models across various business units.
• Architect and develop scalable infrastructure for automated model training, hyperparameter tuning, and deployment.
• Mentor and guide junior engineers, collaborating closely with machine learning engineers to optimize and refine models.
• Own the end‑to‑end systems for model monitoring, maintenance, and retraining to ensure high availability and performance.

• B.Sc. in Machine Learning, Computer Science, Statistics, Mathematics, or related quantitative field.
• 6+ years of experience applying machine learning techniques (e.g., Ensemble learning, deep learning, reinforcement learning, NLP etc.).
• 6+ years of experience with SQL, Spark and scripting languages like Python.
• 6+ years of experience with machine learning frameworks such as Tensor Flow, PyTorch, and Scikit‑learn.
• 4+ years of experience working with cloud platforms and environments like AWS, Microsoft Azure, Databricks, and Kubernetes.
• 4+ years of experience applying machine learning techniques in a production environment for business solutions.

• Machine Learning Algorithms:
  Strong foundation in and deep understanding of advanced machine learning algorithms including supervised and unsupervised learning techniques as well as familiarity with generative models.
• Statistical Modeling:
  Proficiency in statistical modeling including probability theory & hypothesis testing to be able to interrogate, analyze and interpret data effectively.
• Programming
  
  Skills:
  
  Strong programming skills such as proficiency in Python and experience with machine learning frameworks such as Tensor Flow, Keras, and PyTorch. Familiarity with software development best practices, including CI/CD pipelines, containerization (e.g., Docker), and orchestration (e.g., Kubernetes). Deep understanding of MLOps practices, including model versioning, A/B testing, and continuous deployment.
• Cloud Computing Platforms: A deep understanding of cloud computing platforms such as Azure, AWS or GCP, a deep understanding of distributed systems and familiarity with large‑scale data processing technologies (e.g., Spark, Kafka).
• Leadership:
  Proven experience in leading machine…