Staff Machine Learning Engineer

Ethos

. Own the deployment and production lifecycle of models powering product experiences, including coverage recommenders, user-to-channel routing, and agent assignment logic .

Posted 11/4/2026full-timeRemote • United StatesLead💰 $156,000 - $275,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and operationalizing machine learning models, with a strong focus on real-time production environments and marketing measurement. Proficient in building reliable data pipelines and CI/CD infrastructure while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

Machine Learning EngineeringReal-Time Production ML ModelsPython Engineering SkillsFeature Pipeline DevelopmentMarketing Measurement Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringReal-Time Production ML ModelsPythonSQLFeature Pipeline DevelopmentWorkflow OrchestrationML MethodsAttribution ModelingMedia Mix ModelingData Warehouse Tooling

Soft Skills

Strong Communication SkillsCollaboration with Non-Technical StakeholdersMentorship

Tools & Technologies

AWS SageMakerDockerAirflowCI/CD PipelinesData Pipelines

Industry Keywords

InsurtechFintechRegulated Industries

Tech Stack

Tools & technologies

AirflowAWSCloudDockerPythonSQL

About the role

Key responsibilities & impact

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 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 enabling data scientists to ship iterative improvements
Deploy and operationalize marketing models for attribution, incrementality, and media mix
Automate feedback loops to ad platforms
Build reliable data pipelines delivering 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
Contribute through code review, documentation, and mentorship

Requirements

What you’ll need

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
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
Bias toward ownership and closing infrastructure gaps
Ability to balance pragmatic solutions with over-engineered infrastructure tradeoffs
Ability to bridge data science and engineering and work fluently across both disciplines
Ability to raise the team's ML engineering bar through code review, documentation, and mentorship

Benefits

Comp & perks

US benefits details provided at https://www.ethoslife.com/careers/
Bonus, equity, and benefits may apply in addition to the base salary