Staff Machine Learning Engineer, AI Agent Platform

Job description

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.