Staff Machine Learning Engineer

Ethos Life

3d • $156,000 - $275,000

About The Position

Ethos is seeking a Staff Machine Learning Engineer to own the infrastructure that moves models from development into product and marketing workflows. The role partners with data scientists and product engineers to build deployment pipelines, feature stores, monitoring systems, and retraining infrastructure across product and marketing use cases. The ideal candidate combines data science understanding with the ability to build scalable data architecture, considering how models perform in production at scale as well as their predictive performance.

For product ML, the engineer owns the deployment and production lifecycle of models supporting product experiences, including coverage recommenders, user-to-channel routing, and agent assignment logic. The role builds real-time feature pipelines for consumer-facing products and implements monitoring and alerting for data drift, feature quality, and model degradation. It also develops CI/CD and retraining infrastructure that enables data scientists to ship improvements efficiently.

For marketing ML, the engineer deploys and operationalizes attribution, incrementality, and media mix models; automates feedback loops to ad platforms; builds reliable data pipelines for modeling and measurement; and manages model health monitoring and dependencies across the marketing model inventory. The role also raises the team's ML engineering standards through code review, documentation, and mentorship.

This is a full-time position. The US national base salary range is $156,000–$275,000. The range varies by role, level, and location, and individual pay depends on work location and factors such as job-related skills, experience, and relevant education or training. The stated compensation is base salary only and does not include applicable bonus, equity, or benefits.

Requirements

• At least 7 years of experience 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, using AWS SageMaker or an equivalent.
• Strong Python engineering skills, including software design, testing, CI/CD, package development, and SQL.
• Experience building and maintaining feature pipelines, feature stores, and workflow orchestration such as Airflow.
• Familiarity with Docker, container orchestration, and cloud infrastructure.
• Strong communication skills and ability to collaborate with data scientists, product engineers, and non-technical stakeholders.
• Working knowledge of supervised learning, Bayesian models, and causal inference sufficient to debug a model without a data scientist present.
• Experience with marketing measurement, such as attribution, media mix modeling, or ad platform integrations.
• Ability to take ownership of infrastructure gaps and address them.
• Ability to balance infrastructure design with pragmatic solutions and understand the relevant tradeoffs.
• Ability to bridge data science and engineering and work effectively with both teams.

Nice To Haves

• Experience with dbt or data warehouse tooling.
• Experience in insurtech, fintech, or another regulated industry.
• Gets into implementation details while maintaining a broader perspective.

Responsibilities

• Own deployment and production lifecycle for models powering product experiences, including coverage recommenders, user-to-channel routing, and agent assignment logic.
• Build and maintain feature pipelines feeding 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 to help data scientists ship iterative improvements without engineering bottlenecks.
• Deploy and operationalize marketing models, including attribution, incrementality, and media mix models, and automate feedback loops to ad platforms.
• Build reliable data pipelines that provide clean, timely inputs to marketing models and measurement workflows.
• Own model health monitoring and dependency management across the marketing model inventory.
• Collaborate with data scientists, product engineers, and non-technical stakeholders.
• Raise the team's ML engineering bar through code review, documentation, and mentorship.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed