Senior ML Engineer: Build Scalable Production Models

GEICO; Palo Alto (CA); On-site; USD 138,000 - 230,000; Full time

Job summary

GEICO is seeking a Senior Machine Learning Engineer to lead the design, implementation, and deployment of cutting-edge ML models. You will build scalable ML infrastructure, create production-grade services and APIs, and mentor junior engineers while collaborating across Product, Business Units, and Engineering teams.

Ideal candidates have 6+ years in ML and software development, strong cloud/dockers/Kubernetes experience, and a track record of production deployments in large-scale environments.

Qualifications

• BS in CS/ML/Engineering or related technical field.
• 6+ years applying ML techniques (deep learning, RL, NLP) in production.
• 6+ years professional software development in Java/C++/Python/C#.
• 6+ years using ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• 4+ years on cloud platforms (AWS/Azure/GCP) and Docker; Kubernetes experience.
• Proven production ML deployment experience with scalable, reliable systems.

Responsibilities

• Lead the design and implementation of ML models with cross-functional teams.
• Build scalable infrastructure for model training, tuning, and deployment pipelines.
• Write production-grade code to turn ML models into services and APIs.
• Debug and improve model performance; monitor key production metrics.
• Own end-to-end lifecycle: monitoring, retraining, versioning of models.
• Mentor junior engineers and promote best practices in MLOps and software engineering.
• Collaborate with data engineering, software development, and product management teams.
• Stay updated on industry trends and apply new ML techniques and tools.

Skills

ML model design

Python

Distributed systems

MLOps

Cloud & Docker/Kubernetes

Education

B.Sc. in CS/ML/Engineering

M.Sc./Ph.D. in related field

Tools

TensorFlow

PyTorch

Kafka

Airflow

Docker

Kubernetes

Snowflake

Job description

GEICO is seeking a Senior Machine Learning Engineer to lead the design, implementation, and deployment of cutting-edge ML models. You will build scalable ML infrastructure, create production-grade services and APIs, and mentor junior engineers while collaborating across Product, Business Units, and Engineering teams.

Ideal candidates have 6+ years in ML and software development, strong cloud/dockers/Kubernetes experience, and a track record of production deployments in large-scale environments.