Staff ML Engineer: Scalable Models & Real-Time Pipelines

Ethos Life; United States; Remote; USD 156,000 - 275,000; Full time

Job summary

Ethos Life in the United States is seeking a Staff Machine Learning Engineer to own deployment pipelines, feature stores, and retraining infrastructure across product and marketing use cases. You will partner with data scientists and product engineers to scale models from development to production.

The role emphasizes ownership, scalable data architecture, real-time inference, monitoring for data drift, and collaboration across teams.

Qualifications

• 7+ years in ML engineering, MLOps, or software with a strong ML focus.
• Experience deploying and serving real-time production ML models integrated with consumer-facing products (AWS SageMaker or equivalent).
• Strong Python engineering: software design, testing, CI/CD, package development, and SQL.
• Experience building and maintaining feature pipelines, feature stores, and workflow orchestration (Airflow).

Responsibilities

• Own the deployment and production lifecycle of models powering product experiences: coverage recommenders, user-to-channel routing, and agent assignment logic
• Build and maintain feature pipelines that feed real-time inference for consumer-facing product surfaces
• Implement automated monitoring and alerting for data drift, feature quality issues, and model degradation
• Develop CI/CD pipelines and retraining infrastructure that let data scientists ship iterative improvements without engineering bottlenecks
• Deploy and operationalize marketing models: attribution, incrementality, and media mix and automate feedback loops to ad platforms
• Build reliable data pipelines that deliver clean, timely inputs to marketing models and measurement workflows
• Own model health monitoring and dependency management across the marketing model inventory

Skills

ML engineering

Python

Airflow

SageMaker

CI/CD

Data pipelines

SQL

Cloud infra

Tools

Docker

AWS

SageMaker

Job description

Ethos Life in the United States is seeking a Staff Machine Learning Engineer to own deployment pipelines, feature stores, and retraining infrastructure across product and marketing use cases. You will partner with data scientists and product engineers to scale models from development to production.

The role emphasizes ownership, scalable data architecture, real-time inference, monitoring for data drift, and collaboration across teams.