Machine Learning Engineer, Monetization & Decision Systems

About Quizlet:

At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. We're a $1B+ learning platform used by two-thirds of U.S. high school students and half of college students, powering over 1 billion learning interactions each week.

We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We're energized by the potential to power more learners through multiple approaches and various tools.

Let's Build the Future of Learning

Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.

About the Team:

We are looking for Machine Learning Engineers ranging from the Senior to Staff levels (note: leveling decisions made through the interview process).

Within this organization, this role is responsible for the predictive and decisioning models that drive monetization, retention, activation and goal-aligned study guidance. These systems balance immediate impact with long-term user value and must integrate seamlessly into Quizlet's product architecture.

You will lead both the modeling efforts and the technical integration work required to bring complex ML systems into production. This includes designing predictive and prescriptive models (such as conversion propensity, churn risk, LTV, sequential decisioning, and timing optimization) and collaborating closely with product and infrastructure engineering to ensure these models can be safely and cleanly embedded into existing product workflows.

A major part of this role involves identifying dependencies within the product codebase, defining integration contracts with cross-functional partners, and shaping technical solutions that allow ML-driven decisioning to operate reliably, efficiently, and maintainably at scale.

You'll work closely with product managers, data scientists, platform engineers, backend engineers, and fellow ML engineers to deliver ML-driven experiences that drive engagement, satisfaction, and measurable business outcomes.

About the Role:

You will own the full lifecycle of these systems (from problem framing and model development to integration, deployment, and long-term reliability) working closely with product, infrastructure and backend engineering partners. A core responsibility of this role is embedding model-driven decisions into Quizlet's product in a way that is safe, observable, and maintainable, including identifying dependencies, defining clean interfaces, and ensuring robust fallback behavior.

Your work will directly influence monetization, retention, activation and goal-aligned study guidance, requiring you to balance short-term business impact with long-term learner value and product integrity.

We're happy to share that this is an onsite position in either our Denver, San Francisco, Seattle, or NYC. To help foster team collaboration, we require that employees be in the office a minimum of three days per week: Monday, Wednesday, and Thursday and as needed by your manager or the company. We believe that this working environment facilitates increased work efficiency, team partnership, and supports growth as an employee and organization.

In this role, you will:

• Lead the design and development of predictive and prescriptive models (e.g., conversion propensity, churn risk, LTV, uplift, sequential decisioning, and timing optimization) that drive learner-facing decisions across monetization, lifecycle, and study guidance surfaces
• Design and build decisioning and policy models that determine learner-facing actions across product surfaces, including monetization, lifecycle, and study guidance use cases. These systems operate under real-world product constraints and must optimize across multiple, sometimes competing objectives
• Determine when and how to present paywalls, discounts, or value exchanges
• Selecting personalized study modes or interventions based on learner state, intent, and context
• Triggering retention and churn-prevention actions at the appropriate moment
• Balancing short-term conversion and revenue goals with long-term engagement, retention, and learning outcomes
• Prioritize: Multi-objective optimization across monetization, retention, user experience, and learning outcomes, time-aware and eligibility-aware decisioning, rather than static prediction, consistent action selection across sessions, devices, and product surfaces, and an approach that connects offline modeling metrics to online experimental results
• Apply and advance uplift modeling, survival analysis, sequential decisioning, and other policy-based approaches, taking responsibility for bringing these techniques into production-grade systems
• Lead the end-to-end productionization of ML systems, from modeling through integration, ensuring models can be safely, cleanly, and reliably embedded into existing product workflows
• Identify upstr