Staff ML Engineer: Scalable ML Deployments & Pipelines

Ethos; United States; On-site; USD 156,000 - 275,000; Full time

Job summary

Ethos is seeking a Staff Machine Learning Engineer to own deployment pipelines, feature stores, and retraining infrastructure for product and marketing use cases. You will partner with data scientists and product engineers to scale models from development to production, shaping an infrastructure-first mindset across teams.

The role requires deep ML engineering expertise, strong Python skills, and experience with real-time inference, CI/CD, and cloud platforms.

Qualifications

• 7+ years in machine learning engineering, MLOps, or software 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, testing, CI/CD, packaging, 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 with data scientists, product engineers, and non-technical stakeholders.
• Bias toward ownership; you see gaps in the infrastructure and close them.
• Working knowledge of ML methods: supervised learning, Bayesian models, causal inference.
• Marketing measurement: attribution, media mix modeling, or ad platform integrations.
• dbt or data warehouse tooling.
• Experience in insurtech, fintech, or other regulated industries.
• Knows the tradeoffs between over-engineered infrastructure and pragmatic solutions.
• Bridges data science and engineering, trusted by both teams.
• Raises the team\'s ML engineering bar through code review, documentation, and mentorship.

Responsibilities

• 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.

Skills

ML engineering

Python

CI/CD

Airflow

SageMaker

Cloud

Cross-functional

Tools

Docker

Airflow

SageMaker

Job description

Ethos is seeking a Staff Machine Learning Engineer to own deployment pipelines, feature stores, and retraining infrastructure for product and marketing use cases. You will partner with data scientists and product engineers to scale models from development to production, shaping an infrastructure-first mindset across teams.

The role requires deep ML engineering expertise, strong Python skills, and experience with real-time inference, CI/CD, and cloud platforms.