Machine Learning Engineer, AI Agent Platform

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.

The GEICO AI Agent Platform team is seeking an exceptional Staff ML Engineer to build the next generation enterprise AI Agent OS and SDKs. You will design, implement, and maintain scalable backend systems that enable business, product, and engineering teams to build, test, and deploy their own AI agents & workflows. In 2026, the agentic AI landscape is maturing rapidly — with standardized protocols (MCP, A2A), AI agent skill ecosystems, harness engineering, context engineering, and governance-first design becoming table stakes.

You will help GEICO stay at the forefront. The candidate must have excellent communication skills and a proven track record of delivering business value via technical excellence.

• Architect scalable multi-tenant backend systems for AI agent workflows — including AI agent configuration, evaluation, synthetic data generation, workflow simulation & evaluation, MCP server registry, A2A communication infrastructure, and guardrail enforcement layers using AKS, FastAPI, etc.
• Build an enterprise AI agent skill ecosystem — a platform for authoring, publishing, discovering, versioning, and governing reusable skill packages that encode domain expertise into portable modules. Implement an internal skill marketplace with search/discovery, quality scoring, security vetting pipelines, approval workflows, and progressive disclosure loading.
• Implement production‑grade AI agent harnesses — the non-model infrastructure (tool dispatch, context management, error recovery/self‑healing, session state, sub-agent coordination) that makes AI agents reliable for long-running tasks. Design feed forward guides (linters, type checkers, architecture constraints) and feedback sensors (test execution, LLM‑as‑judge, semantic analysis) mixing computational and inferential controls.
• Build and optimize context engineering systems — memory hierarchies (short-term, working, long-term), RAG pipelines, scratchpads, context compaction/summarization, and dynamic skill/tool loading — ensuring AI agents receive the right information at the right time while minimizing token waste.
• Develop observability frameworks (Open Telemetry, distributed tracing) with LLM‑specific telemetry: token usage, latency profiling, hallucination detection, AI agent behavior auditing, and skill execution monitoring.

• Design layered guardrail architectures (input validation, prompt injection defense, PII detection, output verification) with parallelized enforcement for minimal latency impact.
• Implement skill-level governance: security vetting for hidden payloads, credential theft, and data exfiltration risks; authoring standards; conflict resolution; version management; and deprecation workflows.

• Act as tech lead for a sub-team, setting direction and ensuring consistency in design principles. Provide hands‑on mentorship during design reviews, code assessments, and performance tuning.
• Establish engineering standards for ML infrastructure, harness engineering patterns, skill authoring, and deployment practices. Create documentation, runbooks, and training on platform capabilities.
• Collaborate cross-functionally with data scientists, engineers, and product teams. Translate complex technical concepts for diverse stakeholders.

• Bachelor's in CS, Engineering, or related field; advanced…