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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.

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Sr. Staff AI Engineer, AI Agent Platform (Agent & Evaluation Harness)

Why Join GEICO?

GEICO is transforming how AI is built and deployed across the enterprise. As one of the largest insurers in the United States, we are investing heavily in next-generation AI platforms that empower more than 30,000 associates and enhance experiences for millions of customers.

We are looking for a Sr. Staff AI Engineer to push the frontier of what GEICO’s AI Agent Platform can do, with a focus on two core systems: the agent harness that determines how agents plan, reason, use tools, and manage context, and the eval harness that tells us, with evidence, whether they are getting better. The field is moving faster than any job description can capture. Techniques that define best practice today, such as context engineering, MCP-based tool integration, skills, sub-agent orchestration, and LLM-as-judge evaluation, barely existed a few years ago and will look different a year from now. We are looking for someone who thrives in that environment and can move quickly from a new idea to a working prototype to a data-backed conclusion about whether it delivers value.

The Opportunity

The AI Agent Platform team builds use-case-agnostic infrastructure that many business workflows across GEICO build on, from claims and underwriting to internal associate tools. In this role, you’ll own the intelligence layer of that platform: the agent design patterns, context strategies, and evaluation methodology that make agents on the platform reliably good at their jobs.

You’ll work alongside backend platform engineers who own durable execution, scaling, and operations. Your focus is agent behavior and quality, and the science of measuring and improving it, grounded in a solid understanding of distributed systems so that what works in an experiment also works at enterprise scale. Success here comes from experimentation: forming hypotheses, designing experiments, reading traces, and iterating until performance meets the bar for real business workflows.

The model is only the starting point. Your job is to find out what actually works, prove it, and make it reusable.

What You Will Do

Agent Harness

• Design the core agent loop, including planning, reasoning, tool selection and calling, memory, and error recovery, as reusable patterns that generalize across many business workflows.

• Develop context engineering strategies: prompt architecture, context window management, summarization and compaction, memory, and retrieval (RAG) integration.

• Define how agents connect to tools and enterprise systems through the Model Context Protocol (MCP), including tool design, server patterns, and the conventions that make tools reliable for agents to use.

• Design how agents discover, load, and apply skills (packaged instructions, scripts, and resources for specific tasks) so capabilities built once can be reused across many workflows.

• Explore sub-agent and multi-agent patterns, and decide with evidence what belongs in the platform.

• Evaluate new frontier and open-weight models as they are released, and understand their tradeoffs in capability, latency, and cost for platform use.

Evaluation Harness

• Design evaluation methodology: task-level metrics, rubrics, golden datasets, LLM-as-judge graders, and human review, with automated graders calibrated against human judgment.

• Build the eval harness that lets any team define, run, and compare evaluations for their agents, spanning offline benchmarks, simulated users and environments, and online quality monitoring.

• Make eval results trustworthy by accounting for variance, statistical significance, dataset contamination, and grader bias.

• Diagnose failure m…