Staff Machine Learning Engineer

Overview

Own the infrastructure that moves machine learning models from development into product and marketing workflows, building deployment pipelines, feature stores, monitoring and retraining systems.

What you'll do

• 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
• 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

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
• 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
• 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

Nice to have

• Marketing measurement: attribution, media mix modeling, or ad platform integrations
• dbt or data warehouse tooling
• Experience in insurtech, fintech, or other regulated industries

Details

• Full-time, remote in the US
• US national base salary range $156,000 - $275,000 (base only; excludes bonus, equity, and benefits)