Lead Machine Learning Engineer — Production & MLOps

Government Employees Insurance Company; Palo Alto (CA); On-site; USD 115,000 - 230,000; Full time

Job summary

GEICO is seeking an experienced ML/ML Ops engineer to design and implement scalable machine learning models and delivery systems. You will collaborate with product, data engineering, and software teams to deploy reliable ML services and APIs in production.

The role emphasizes model monitoring, retraining, and continuous improvement across cloud platforms and containerized environments, with mentorship for junior engineers in a dynamic, equitable workplace.

Qualifications

• 6+ years applying machine learning techniques in production environments.
• Experience with deep learning, reinforcement learning, and NLP.
• Proficiency with cloud platforms (AWS/Azure/GCP) and containerization (Docker, Kubernetes).
• Experience deploying ML models at scale with CI/CD and monitoring.
• Strong software development background in Java, C++, or Python.

Responsibilities

• Lead the design and implementation of ML models across product and engineering teams.
• Build scalable infrastructure for training, tuning, and deployment pipelines.
• Write production-grade code and expose ML models as APIs and services.
• Optimize model performance and resolve production issues with metrics.
• Own end-to-end model lifecycle including monitoring and retraining.
• Guide junior engineers and promote best practices in ML and software engineering.
• Collaborate with data engineering, software, and product management teams.
• Stay updated with industry trends and new ML/system engineering tools.

Skills

ML Models

Model Deployment

Python

Java/C++

Cloud Platforms

MLOps

CI/CD

Education

B.Sc. in Computer Science / Engineering / ML

Tools

Snowflake

Kafka

PostgreSQL

MongoDB

Spark

Airflow

Kubernetes

Docker

Job description

GEICO is seeking an experienced ML/ML Ops engineer to design and implement scalable machine learning models and delivery systems. You will collaborate with product, data engineering, and software teams to deploy reliable ML services and APIs in production.

The role emphasizes model monitoring, retraining, and continuous improvement across cloud platforms and containerized environments, with mentorship for junior engineers in a dynamic, equitable workplace.