Senior Machine Learning Engineer, AI Agent Platform

GEICO

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: New York, NY
Salary: $105,000–$230,000 / yr
Posted: 98 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $216k

This role $168k

$86k most similar roles pay here $281k

This role pays less than 82% of similar roles. Most pay $185,000–$246,271 — the shaded band above. At the midpoint, this role pays about $168k versus about $216k 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 · Senior Machine Learning Engineer, AI Agent Platform

Role Posting Log in to save

Senior Machine Learning Engineer, AI Agent Platform joins the AI organization to develop a virtual assistant platform enhancing internal productivity and customer experience. This role involves building multi-tenant services for LLM-based agents, including skill ecosystems, harness components like tool dispatch and session state management, and context engineering systems involving RAG pipelines and memory management. The engineer will implement guardrail systems for PII detection and prompt injection defense while collaborating with cross-functional teams to manage project backlogs. Required technical skills include proficiency in Java, C++, Python, or C#, alongside experience with OpenSearch, Milvus, Snowflake, Kafka, PostgreSQL, Cassandra, Spark, Ray, Airflow, Temporal, and Redis/Valkey. The role also requires expertise in Kubernetes, CI/CD pipelines, and evaluation systems for LLMs using GPU-powered infrastructure to ensure the reliability of long-running generative AI workflows.

What does a Machine Learning Engineer earn in New York?

Median $216000 from 43 postings across 13 companies.

See salary data

Skills

Generative AI LLM RAG LangGraph AutoGen CrewAI Python Java C++ C# Kubernetes CI/CD OpenSearch Milvus Snowflake Kafka PostgreSQL Cassandra Spark Ray Airflow Temporal Redis Valkey LangSmith Langfuse

What you'll do

Develop multi-tenant services to support the building, testing, and deployment of LLM-based AI agents.
Build an enterprise AI agent skill ecosystem including tools for authoring, publishing, and versioning reusable skill packages.
Maintain AI agent harness components such as tool dispatch, context management, error recovery, and session state.
Develop context engineering systems to manage memory, RAG pipelines, and dynamic tool loading for LLM windows.
Implement guardrail components including input validation, prompt injection defense, PII detection, and output verification.
Create bounded autonomy systems with human-in-the-loop escalation paths and audit trail infrastructure.
Evaluate software platforms and frameworks to balance build versus buy decisions based on speed and maintainability.
Mentor junior engineers through code reviews and design sessions to promote AI engineering best practices.

What we're looking for

Bachelor's degree or above in Computer Science, Engineering, Statistics, or a related field.
5+ years of professional software development experience with at least two general-purpose programming languages like Java, C++, Python, or C#.
4+ years of experience designing and building AI/ML platforms using open-source or cloud-agnostic components such as search engines, data warehouses, and streaming platforms.
3+ years of experience contributing to end-to-end software development lifecycles including version control, CI/CD pipelines, Kubernetes, and production support.
3+ years of experience building evaluation and observability systems for AI/ML models and LLMs on GPU-powered infrastructure.
Familiarity with harness engineering concepts such as memory management, RAG, context/tool management, and guardrails.
Experience building conversational experiences, agentic workflows, and multi-agent orchestration frameworks like LangGraph or CrewAI.
Experience implementing AI agent guardrails, skill systems, and LLM observability platforms.

Save