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About This Role
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.
Staff Machine Learning Engineer, AI Agent Platform
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.
Key Responsibilities
Platform Engineering
- 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 feedforward 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 (OpenTelemetry, distributed tracing) with LLM\-specific telemetry: token usage, latency profiling, hallucination detection, AI agent behavior auditing, and skill execution monitoring.
AI Safety, Governance \& Guardrails
- 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.
Technical Leadership
- 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.
Qualifications
Technical Skills
- Bachelor's in CS, Engineering, or related field; advanced degree highly desirable.
- 6\+ years designing, implementing, and maintaining multi\-tenant AI/ML systems in production.
- 6\+ years with cloud platforms (Azure, AWS) and backend systems (Kubernetes, Temporal, OpenSearch, PostgreSQL, Redis, Neo4j). Deep understanding of Docker, Prometheus, and OpenTelemetry.
- Deep proficiency in Python, Java, or Go. Extra credit for effectively leveraging AI coding tools (Cursor, Claude Code, GitHub Copilot).
- Proficiency in AI/ML and agentic frameworks (TensorFlow, PyTorch, LangGraph, CrewAI, AutoGen).
Leadership Skills
- Demonstrated track record mentoring engineers and leading technical initiatives.
- Excellent communication across diverse seniority levels and professional backgrounds.
Preferred Specialized Skills
- Experience with harness engineering concepts and practices such as tool dispatch, error recovery, session state, permissions, sub\-agent coordination, planning \& reasoning w. feedback loops, etc..
- Experience designing AI agent skill systems — reusable capability packages, skill registries/marketplaces with discovery, versioning, security vetting, and governance controls.
- Hands\-on experience with MCP (server development, registries) and A2A (AI agent card discovery, task delegation).
- Experience with LLM observability (LangSmith, Langfuse, Arize Phoenix) and guardrail systems (prompt injection defense, PII scanning, skill\-level security auditing).
- Experience with multi\-agent orchestration, both open\-source (Llama, Qwen, Mistral) and proprietary (GPT, Claude) LLMs, and no\-code/low\-code AI agent development environments.
If you are passionate about pushing the boundaries of generative AI platforms, thrive in a hands\-on technical leadership role, and enjoy solving complex, large\-scale problems, we encourage you to apply.
Annual Salary
$115,000\.00 \- $260,000\.00
The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate’s work experience, education and training, the work location as well as market and business considerations.
GEICO will consider sponsoring a new qualified applicant for employment authorization for this position. The GEICO Pledge:
Great Company: Protecting customers through life’s twists and turns with innovation and integrity.
Great Careers:Personalized development programs, mentorship, and certification assistance.
Great Culture:Inclusive and collaborative culture rooted in shared success.
Great Rewards:Competitive pay, benefits, and flexibility to support your well\-being and future.
The equal employment opportunity policy of the GEICO Companies provides for a fair and equal employment opportunity for all associates and job applicants regardless of race, color, religious creed, national origin, ancestry, age, gender, pregnancy, sexual orientation, gender identity, marital status, familial status, disability or genetic information, in compliance with applicable federal, state and local law. GEICO hires and promotes individuals solely on the basis of their qualifications for the job to be filled.
GEICO reasonably accommodates qualified individuals with disabilities to enable them to receive equal employment opportunity and/or perform the essential functions of the job, unless the accommodation would impose an undue hardship to the Company. This applies to all applicants and associates. GEICO also provides a work environment in which each associate is able to be productive and work to the best of their ability. We do not condone or tolerate an atmosphere of intimidation or harassment. We expect and require the cooperation of all associates in maintaining an atmosphere free from discrimination and harassment with mutual respect by and for all associates and applicants.
Salary Context
This $115K-$260K range is below the median for AI Agent Developer roles in our dataset (median: $212K across 45 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 3,824 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At GEICO, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $252,000 based on 90 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($187K) sits 26% below the category median. Disclosed range: $115K to $260K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
GEICO AI Hiring
GEICO has 21 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Software Engineer, Research Scientist. Positions span Palo Alto, CA, US, New York, NY, US, Bethesda, MD, US. Compensation range: $215K - $350K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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