Staff Machine Learning Engineer
New
E
EthosLife insurance technology
Location: Remote USFull-TimeStaff
Salary156,000 - 275,000 USD per year
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Job Details
- Experience
- 7+ years in machine learning engineering, MLOps, or software engineering with a strong ML focus
- Required Skills
- DockerPythonSQLAirflowMLOps
Requirements
- Have 7+ years of experience in machine learning engineering, MLOps, or software engineering with a strong ML focus.
- Have experience deploying and serving real-time production ML models integrated with consumer-facing products, using AWS SageMaker or equivalent.
- Bring strong Python engineering skills, including software design, testing, CI/CD, and package development.
- Have SQL skills.
- Have experience building and maintaining feature pipelines, feature stores, and workflow orchestration such as Airflow.
- Be familiar with Docker, container orchestration, and cloud infrastructure.
- Have working knowledge of supervised learning, Bayesian models, and causal inference.
- Have marketing measurement experience in attribution, media mix modeling, or ad-platform integrations.
- Have experience with dbt or data warehouse tooling.
- Experience in insurtech, fintech, or another regulated industry is listed as a qualification.
Responsibilities
- Own the deployment and production lifecycle of models powering product experiences, including coverage recommenders, user-to-channel routing, and agent assignment logic.
- Build and maintain feature pipelines for real-time inference on consumer-facing product surfaces.
- Implement monitoring and alerting for data drift, feature quality issues, and model degradation.
- Develop CI/CD pipelines and retraining infrastructure for iterative model improvements.
- Deploy and operationalize marketing models for attribution, incrementality, and media mix.
- Automate feedback loops to ad platforms.
- Build reliable data pipelines for marketing models and measurement workflows.
- Own model health monitoring and dependency management across the marketing model inventory.
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