AI Machine Learning Engineer II

What you'll do

• Lead end-to-end machine learning initiatives from data preparation and model development through production deployment and monitoring.
• Build and deploy scalable ML solutions for batch or real-time inference using robust MLOps workflows.
• Design and analyze A/B tests and validation frameworks to measure incremental customer and business impact.
• Develop advanced models for propensity, uplift, recommendation, ranking, contextual bandits, and pricing.
• Partner with engineering, product, and data teams to integrate models into reliable production systems.
• Create agentic tools and automated workflows to streamline model development and improve MLOps productivity.
• Perform large-scale data processing and feature engineering using SQL, BigQuery, or Spark.

What we're looking for

• A technical degree in a quantitative field is helpful, but equivalent practical experience is also accepted.
• A Master's or PhD in a quantitative field is preferred.
• At least five years of professional experience in applied machine learning, data science, ML engineering, or applied statistics.
• Experience building and evaluating models using large-scale behavioral, transactional, product, marketing, or customer data.
• Experience designing experiments, defining success metrics, measuring incrementality, and translating findings into business decisions.
• Proficiency in Python with experience in common ML frameworks, supervised learning, and hyperparameter tuning.
• Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data processing and feature engineering.
• Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, optimization, or lifecycle decisioning (preferred).