Staff Machine Learning Engineer, Monetization & Decision Systems

At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our $1B+ learning platform serves tens of millions of students every month,  including two-thirds of U.S. high schoolers and half of U.S. college students, powering over 2 billion learning interactions monthly.

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.

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

 

The Personalization & Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content, activities, and user experiences that best fit their goals, while also optimizing for business metrics that support long-term sustainability. We power recommendation and search systems across multiple surfaces, such as the home feed, search results, and adaptive study modes, as well as decision systems in ads and notifications that determine the timing and nature of key interventions.

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.

 

As a Staff Machine Learning Engineer on the Personalization & Recommendations team, 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.

 

 

As a Staff Machine Learning Engineer on the Personalization & Recommendations team, you will lead the development of ML systems that decide what action Quizlet should take for a learner, when that action should occur, and under what constraints. This role focuses on action selection and policy design rather than content ranking alone, and requires deep ownership of both modeling and production integration.

 

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 our San Francisco office. 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.