Machine Learning Engineer

Introduction

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.

GEICO is seeking a Staff Machine Learning Engineer to help shape how Generative AI enhances customer and associate experiences across the enterprise. This is a hands‑on technical role who will be leading the strategy, architecture, and delivery of ML systems for the Claims organization—designing predictive models, robust data/feature pipelines, and production‑grade MLOps to drive measurable business outcomes.

You will work alongside engineering teams, data scientists, and product leaders to design, build, and integrate AI‑powered capabilities that automate workflows, improve decision‑making, and elevate user experience. You will contribute to a culture of learning, curiosity, and innovation while growing your expertise in cutting‑edge AI technologies.

• Staff+ individual contributor role focused on end‑to‑end ML: data and feature engineering, modeling, deployment, monitoring, and continuous improvement.
• Partner with Claims Operations, Product, and Engineering to deliver ML capabilities such as severity/triage predictions, claim outcome forecasting, and automation accelerators.
• GenAI (e.g., LLMs and agentic workflows) may be leveraged where it augments ML systems; strong ML depth is primary.

• Work on the ML platform architecture: data/feature pipelines, experiment tracking, model registries, serving layers, offline/online evaluation, and observability.
• Define standards for reliability, performance, cost efficiency, security, governance, and model risk management across ML services.
• Lead design and implementation of models across classical ML and deep learning (e.g., gradient boosted trees, sequence models, Transformers for tabular/time‑series/NLP where relevant).
• Translate business goals into measurable ML objectives and experiment plans; ensure robust offline metrics and real‑world impact.
• Build scalable training and inference pipelines; establish CI/CD for ML, automated evaluations, canary releases, and rollback strategies.
• Implement monitoring for data quality, drift, fairness, latency, reliability, and cost; lead incident response and postmortems.
• Partner with Claims, Product, Data Science, Platform/SRE, Security, and Legal/Compliance to gather requirements, define scope, and prioritize backlogs.
• Maintain pragmatic technical roadmaps balancing business outcomes, release timelines, and engineering excellence.
• Own build‑vs‑buy decisions and tooling/service selection (speed to market, extensibility, TCO); guide platform evolution with clear architectural principles.
• Lead experienced engineers through complex platform implementations; drive system‑wide architectural improvements and reliability practices.
• Mentor engineers and junior tech leads; codify best practices; contribute to internal documentation and promote enterprise‑wide ML standards.
• Where appropriate, collaborate on retrieval‑augmented workflows, prompt/context management, and LLM evaluation and safety guardrails to complement ML systems.

• Bachelor’s degree or above in Computer Science, Engineering, Statistics, or related field.
• 5+ years of professional software development experience using at least two general‑purpose languages (e.g., Java, C++, Python, C#).
• 5+ years architecting, designing, and building multi‑component ML platforms leveraging open‑source/cloud‑agnostic components:
  • Search/vector:
    Elastic Search, Qdrant (as applicable to ML features and retrieval)
  • Data warehouse/lakehouse:
    Snowflake; familiarity with Parquet/Delta/Iceberg
  • Streaming:
    Kafka; plus Flink/Spark Streaming experience
  • Data stores:
    Postgre
    
    S…