Staff Machine Learning Engineer, Monetization & Decision Systems
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The role
Lead the end-to-end development and productionization of ML systems that decide what action to take for learners, when to act, and under what constraints, with a focus on monetization, retention, activation, and study guidance.
Design predictive and prescriptive models (e.g., conversion propensity, churn risk, LTV, uplift) and policy frameworks that operate under real-world product constraints and multiple objectives.
Own risk-aware integration with product, backend, and infrastructure, defining clean interfaces, API contracts, data schemas, and robust fallback strategies.
Build and maintain end-to-end pipelines for feature engineering, training, evaluation, deployment, and monitoring, ensuring training–serving consistency and low latency.
Develop evaluation frameworks linking offline metrics to online experiments, and communicate trade-offs and risks to technical and non-technical stakeholders.
Provide technical leadership, mentorship, and a long-term strategy for scalable, observable ML decisioning across surfaces.
Design predictive and prescriptive models (e.g., conversion propensity, churn risk, LTV, uplift) and policy frameworks that operate under real-world product constraints and multiple objectives.
Own risk-aware integration with product, backend, and infrastructure, defining clean interfaces, API contracts, data schemas, and robust fallback strategies.
Build and maintain end-to-end pipelines for feature engineering, training, evaluation, deployment, and monitoring, ensuring training–serving consistency and low latency.
Develop evaluation frameworks linking offline metrics to online experiments, and communicate trade-offs and risks to technical and non-technical stakeholders.
Provide technical leadership, mentorship, and a long-term strategy for scalable, observable ML decisioning across surfaces.
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