[Hiring] Staff Machine Learning Engineer @Ethos Life

Role Description

We're looking for a Staff Machine Learning Engineer to own the infrastructure that gets our models from development into product and marketing workflows. You'll partner with data scientists and product engineers to build deployment pipelines, feature stores, monitoring systems, and retraining infrastructure across both product and marketing use cases. The right candidate is a data scientist that can also build scalable data architecture: you care about how a model will run at scale, not just how it performs on AUC.

Duties and Responsibilities:

• Product ML Engineering
  • 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.
• Marketing ML Engineering
  • 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.

Qualifications

• 7+ years 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 (AWS SageMaker or equivalent).
• Strong Python engineering skills: software design, testing, CI/CD, package development, 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 fluidly with data scientists, product engineers, and non-technical stakeholders.
• Bias toward ownership; you see a gap in the infrastructure and close it.
• Working knowledge of ML methods: supervised learning, Bayesian models, causal inference, enough to debug a model without a data scientist in the room.
• Marketing measurement: attribution, media mix modeling, or ad platform integrations.
• dbt or data warehouse tooling.
• Experience in insurtech, fintech, or other regulated industries.
• Gets in the weeds without losing the bigger picture.
• Knows the tradeoffs between over-engineered infrastructure and pragmatic solutions, and picks the right one.
• Bridges data science and engineering, fluent in both worlds and trusted by both teams.
• Raises the team's ML engineering bar through code review, documentation, and mentorship.

Requirements

• #LI-Remote
• #LI-TL1

Benefits

• The US national base salary range for this full-time position is $156,000 - $275,000.
• Our salary ranges are determined by role, level, and location.
• The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations.
• Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
• Please note that the compensation details listed in US role postings reflect the base salary only and do not include applicable bonus, equity, or benefits.