ethoslife · Remote US
Skills: Python, SQL, AWS, Docker, CI/CD
About Ethos Ethos (Nasdaq: $LIFE) is a leading life insurance technology company on a mission to protect families by democratizing access to life insurance and empowering agents at scale. With its robust three-sided technology platform, Ethos is transforming the life insurance experience for consumers, agents, and carriers alike. Ethos offers instant, accessible products and a seamless online process that requires no medical exams and just a few health questions; it eliminates traditional barriers, making it easier than ever for everyone to protect their families. Ethos is redefining how life insurance is bought, sold, and underwritten. 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 wor…
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