Senior Staff Software Engineer - Developer Experience
GEICOAbout the role
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 an experienced and highly skilled Senior Staff Software Engineer to join our AI Assisted Developer Experience team. You will be part of a team of engineers that will develop the discipline, frameworks, tooling, for building software aided by AI, in a highly secure, performant, auditable, and repeatable fashion.
Position Responsibilities
Platform & Tooling
- Design and own the internal developer platform — CI/CD pipelines, local dev environments, build systems, and deployment workflows — with measurable impact on DORA metrics
- Evaluate, integrate, and govern AI coding assistants (e.g. GitHub Copilot, Cursor, Claude Code) across engineering teams, including rollout strategy, usage policy, and ROI measurement
- Build and maintain AI-powered tooling: code generation scaffolds, intelligent PR reviewers, automated documentation generators, and context-aware onboarding assistants
- Define and enforce standards for IDE configuration, linting, formatting, and code generation prompts across polyglot environments
Developer Productivity
- Identify and eliminate friction in the software development lifecycle through data — measure time-to-first-PR, build times, incident MTTR, and cognitive load
- Architect agentic workflows that automate repetitive engineering tasks (boilerplate generation, dependency upgrades, test scaffolding, changelog authoring)
- Own the inner dev loop: hot reload, local service mocking, test data generation, and environment parity between local and production
- Drive adoption of prompt engineering best practices and RAG-based internal knowledge tooling (runbooks, architecture docs, incident history)
Architecture & Standards
- Set technical direction for developer-facing APIs, SDKs, and CLI tooling consumed by internal engineering teams
- Establish LLM integration patterns — context window management, prompt versioning, output validation, hallucination guardrails — as reusable internal libraries
- Define AI-assisted code review standards: what tooling enforces, what humans own, and escalation paths for ambiguous cases
- Lead architectural reviews with a DX lens, ensuring new systems are observable, testable, and locally runnable from day one
Cross-Functional Leadership
- Partner with security and compliance to govern AI tool usage — data residency, secret handling, acceptable use policies for LLM-generated code
- Represent developer experience in roadmap planning, translating engineering pain points into prioritized platform investments
- Mentor staff and senior engineers on AI-augmented development practices; run internal enablement programs and brown bags
- Influence hiring by defining technical bar for DX-adjacent roles and contributing to interview design
Measures of Success
- Reduction in time-to-production for new services
- Increase in developer satisfaction scores (quarterly survey)
- Reduction in toil hours per engineer per sprint
- Adoption rate and productivity lift from AI tooling
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