Staff Machine Learning Engineer, AI Agent Platform

GEICO

Confirmed live 2 days ago Trusted

Remote

Quick summary

Work type: Remote
Location: New York, NYPalo Alto, CABethesda, MDSeattle, WA
Salary: $115,000–$260,000 / yr
Posted: 98 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $225k

This role $188k

$97k most similar roles pay here $281k

This role pays less than 80% of similar roles. Most pay $196,012–$254,562 — the shaded band above. At the midpoint, this role pays about $188k versus about $225k for comparable roles.

Based on 240 similar postings.

Employer

About GEICO

GEICO (Government Employees Insurance Company) is one of the largest auto insurers in the United States, offering affordable auto, home, renters, and other personal insurance products. Industry: Insurance

GEICO currently has 68 open roles on FindRole.

Listed pay typically runs $112,500–$230,000 across 68 roles with salary data.

Most-posted roles

View all roles at GEICO

At a glance

TL;DR · Staff Machine Learning Engineer, AI Agent Platform

Role Posting Log in to save

Staff Machine Learning Engineer, AI Agent Platform joins the GEICO AI Agent Platform team to build the next generation enterprise AI Agent OS and SDKs. This role involves architecting scalable multi-tenant backend systems for agent workflows, including configuration, evaluation, synthetic data generation, and guardrail enforcement. The engineer will develop an internal skill marketplace, implement production-grade harness engineering for reliable long-running tasks, and optimize context engineering systems like RAG pipelines and memory hierarchies. Key responsibilities include designing observability frameworks using OpenTelemetry and implementing safety protocols against prompt injection and PII leaks. The role requires expertise in Python, Java, or Go, along with experience in Kubernetes, Docker, and PostgreSQL. Candidates must be proficient in agentic frameworks like LangGraph or CrewAI to solve complex problems involving multi-agent orchestration and automated task delegation within a maturing AI landscape.

What does a Machine Learning Engineer earn in New York?

Median $216000 from 43 postings across 13 companies.

See salary data

Skills

Python Java Go Kubernetes AKS FastAPI Docker Prometheus OpenTelemetry PostgreSQL Redis Neo4j OpenSearch Temporal TensorFlow PyTorch LangGraph CrewAI AutoGen RAG LLM MCP A2A

What you'll do

Architect scalable multi-tenant backend systems for AI agent workflows using technologies like AKS and FastAPI.
Build an enterprise AI agent skill ecosystem for authoring, publishing, and governing reusable domain expertise modules.
Implement production-grade infrastructure for tool dispatch, context management, error recovery, and sub-agent coordination.
Develop context engineering systems including RAG pipelines, memory hierarchies, and dynamic tool loading to optimize token usage.
Create observability frameworks with LLM-specific telemetry for hallucination detection, latency profiling, and behavior auditing.
Design layered guardrail architectures to defend against prompt injections and ensure PII detection.
Act as a technical lead by mentoring engineers and establishing engineering standards for ML infrastructure.
Translate complex technical concepts into actionable information for diverse stakeholders and cross-functional teams.

What we're looking for

Bachelor's degree in Computer Science, Engineering, or a related field is required; an advanced degree is highly desirable.
6+ years of experience designing, implementing, and maintaining multi-tenant AI/ML systems in production.
6+ years of experience with cloud platforms (Azure, AWS) and backend systems including Kubernetes, Temporal, OpenSearch, PostgreSQL, Redis, and Neo4j.
Deep proficiency in Python, Java, or Go.
Proficiency in AI/ML and agentic frameworks such as TensorFlow, PyTorch, LangGraph, CrewAI, or AutoGen.
Demonstrated track record of mentoring engineers and leading technical initiatives.
Experience with harness engineering, LLM observability tools (LangSmith, Langfuse, Arize Phoenix), and guardrail systems.
Experience with MCP server development, A2A communication infrastructure, and multi-agent orchestration.

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