Staff ML Engineer: Scale Real-Time, Product-Facing Models

Ethos Life; San Francisco (CA); Sur place; USD 156 000 - 275 000; Plein temps

Résumé du poste

Ethos Life is hiring a Staff Machine Learning Engineer to own deployment infrastructure, get models from development into product workflows, and partner with data scientists and product engineers to build pipelines, feature stores, monitoring, and retraining systems. You will shape real-time inference and marketing model delivery with strong ownership mindset.

You will deploy production models powering product experiences, build reliable data pipelines, and implement automated monitoring to

Qualifications

• 7+ years in machine learning engineering, MLOps, or software engineering 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 skills: software design, testing, CI/CD, package development, and SQL
• Experience building and maintaining feature pipelines, feature stores, and workflow orchestration (e.g., Airflow)
• Familiarity with Docker, container orchestration, and cloud infrastructure
• Strong communication skills; able to collaborate fluidly with data scientists, product engineers, and non-technical stakeholders
• Bias toward ownership; you see a gap in the infrastructure and close it
• Working knowledge of ML methods: supervised learning, Bayesian models, causal inference, enough to debug a model without a data scientist in the room
• Marketing measurement: attribution, media mix modeling, or ad platform integrations
• dbt or data warehouse tooling
• Experience in insurtech, fintech, or other regulated industries

Responsabilités

• 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

Connaissances

Machine learning engineering

Real-time deployment

Python

Airflow

Docker

Marketing measurement

dbt

Regulated industries

Outils

Airflow

Docker

dbt

Description du poste

Ethos Life is hiring a Staff Machine Learning Engineer to own deployment infrastructure, get models from development into product workflows, and partner with data scientists and product engineers to build pipelines, feature stores, monitoring, and retraining systems. You will shape real-time inference and marketing model delivery with strong ownership mindset.

You will deploy production models powering product experiences, build reliable data pipelines, and implement automated monitoring to