Staff Machine Learning Engineer
About the role
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 and Skills:
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
#LI-Remote #LI-TL1
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.
You can find further details of our US benefits at https://www.ethoslife.com/careers/