Staff Machine Learning Engineer

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

Job summary

Ethoslife seeks an experienced machine-learning engineering leader to own deployment pipelines and production lifecycle for ML models. You will partner with data scientists and product engineers to build robust deployment, monitoring, and retraining capabilities in customer-facing applications.

The role requires building feature pipelines, CI/CD workflows, and reliable data pipelines across marketing and product domains, while collaborating with cross-functional teams and ensuring scalable,

Qualifications

• At least seven years of experience in machine-learning engineering, MLOps, or software engineering with a strong ML focus.
• Experience serving real-time production ML models integrated with consumer products, using SageMaker or an equivalent platform.
• Strong Python engineering skills, including software design, testing, CI/CD, package development, and SQL.
• Experience with feature pipelines or stores and workflow orchestration tools such as Airflow.
• Familiarity with Docker, container orchestration, and cloud infrastructure.
• Ability to collaborate across engineering, data science, and non-technical teams, with strong ownership and communication.
• Working knowledge of supervised learning, Bayesian methods, and causal inference sufficient to investigate model issues.

Responsibilities

• Deploy and operate models used for product decisions such as recommendations, channel routing, and agent assignment.
• Build and maintain feature pipelines that support real-time inference in consumer-facing experiences.
• Establish monitoring and alerts for data drift, feature quality, and model performance.
• Develop CI/CD and retraining workflows so data scientists can release improvements efficiently.
• Operationalize marketing models and measurement workflows, including feedback integrations with advertising platforms.
• Maintain dependable data pipelines, model health monitoring, and dependencies across the model portfolio.

Skills

ML engineering

MLOps

Python

CI/CD

Airflow

Docker

SQL

Cloud infra

Cross-team collaboration

Supervised learning

Tools

SageMaker

Docker

Airflow

AWS

Job description

Role overview

Own the infrastructure and production lifecycle that move machine-learning models from development into customer-facing products and marketing workflows. You’ll partner with data scientists and product engineers to build deployment pipelines, feature data systems, monitoring, and retraining capabilities, balancing model performance with reliable operation at scale.

Responsibilities

• Deploy and operate models used for product decisions such as recommendations, channel routing, and agent assignment.
• Build and maintain feature pipelines that support real-time inference in consumer-facing experiences.
• Establish monitoring and alerts for data drift, feature quality, and model performance.
• Develop CI/CD and retraining workflows so data scientists can release improvements efficiently.
• Operationalize marketing models and measurement workflows, including feedback integrations with advertising platforms.
• Maintain dependable data pipelines, model health monitoring, and dependencies across the model portfolio.

Requirements

• At least seven years of experience in machine-learning engineering, MLOps, or software engineering with a strong ML focus.
• Experience serving real-time production ML models integrated with consumer products, using SageMaker or an equivalent platform.
• Strong Python engineering skills, including software design, testing, CI/CD, package development, and SQL.
• Experience with feature pipelines or stores and workflow orchestration tools such as Airflow.
• Familiarity with Docker, container orchestration, and cloud infrastructure.
• Ability to collaborate across engineering, data science, and non-technical teams, with strong ownership and communication.
• Working knowledge of supervised learning, Bayesian methods, and causal inference sufficient to investigate model issues.

Nice to have

• Experience with marketing measurement, such as attribution or media mix modeling, and advertising platform integrations.
• Familiarity with dbt or data warehouse tools, or experience in insurance, financial technology, or another regulated field.
• A record of raising engineering standards through reviews, documentation, or mentorship.

Benefits and work setup

• Remote US role with a listed national base salary range of $156,000–$275,000; compensation varies by location and other factors. The range excludes any applicable bonus, equity, and benefits.