Staff Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Take charge of the deployment and production lifecycle of models that enhance product experiences, including coverage recommenders, user-to-channel routing, and agent assignment logic.

• Develop and maintain feature pipelines that support real-time inference for consumer-facing product surfaces.

• Establish automated monitoring and alerting systems for data drift, feature quality issues, and model degradation.

• Create CI/CD pipelines and retraining infrastructure that enable data scientists to implement iterative enhancements.

• Deploy and operationalize marketing models focused on attribution, incrementality, and media mix strategies.

• Automate feedback loops to advertising platforms.

• Construct reliable data pipelines that provide clean and timely inputs to marketing models and measurement workflows.

• Oversee model health monitoring and dependency management across the entire marketing model inventory.

• Collaborate with data scientists, product engineers, and stakeholders who may not have a technical background.

• Contribute through code reviews, documentation efforts, and mentorship.

⛳️ Requirements

• Over 7 years of experience in machine learning engineering, MLOps, or software engineering with a strong focus on machine learning.

• Proven experience in deploying and serving real-time production machine learning models that are integrated with consumer-facing products (AWS SageMaker or equivalent).

• Strong proficiency in Python engineering, including software design, testing, CI/CD, package development, and SQL.

• Experience in building and maintaining feature pipelines, feature stores, and workflow orchestration (e.g., Airflow).

• Familiarity with Docker, container orchestration, and cloud infrastructure.

• Excellent communication skills, capable of collaborating effectively with data scientists, product engineers, and non-technical stakeholders.

• Working knowledge of machine learning methods, including supervised learning, Bayesian models, and causal inference, sufficient to debug a model without a data scientist present.

• Experience in marketing measurement, including attribution, media mix modeling, or ad platform integrations.

• Proficiency in dbt or data warehouse tools.

• Background in insurtech, fintech, or other regulated industries.

• A strong sense of ownership and a proactive approach to closing infrastructure gaps.

• Ability to balance practical solutions with the complexities of over-engineered infrastructure.

• Capacity to bridge the gap between data science and engineering, working fluently across both fields.

• Ability to elevate the team's machine learning engineering standards through code reviews, documentation, and mentorship.

🏝️ Benefits

• Details regarding US benefits can be found at https://www.ethoslife.com/careers/.

• Bonus, equity, and additional benefits may apply on top of the base salary.