Staff 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. 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. 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: 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. About the Role: 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. 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

You will work on problems such as: determining when and how to present paywalls, discounts, or value exchanges, selecting personalized study modes or interventions based on learner state and intent, triggering retention or churn-prevention actions at the right moment, and balancing immediate conversion or revenue with long-term engagement and learning outcomes

This role emphasizes: multi-objective optimization across monetization, retention, and user experience, timing- and eligibility-aware decisioning rather than static predictions, and consistent action selection across sessions and surfaces

Evaluation approaches that connect offline modeling metrics to online experimental outcomes

Apply advanced techniques such as uplift modeling, survival analysis, sequential decisioning, and other policy-based approaches, bringing them into production in collaboration with cross-functional partners

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 upstream and downstream dependencies within the product codebase and data ecosystem, and proactively address integration risks

Define and negotiate clean integration boundaries, including API contracts, data interfaces, decision schemas, and fallback strategies, in collaboration with product and infrastructure engineering

Partner closely with Infrastructure Engineering to design scalable, resilient, and observable model-serving paths that integrate with Quizlet’s application stack

Embed model-driven decisioning logic into backend and product flows in ways that are maintainable, testable, and compatible with existing systems

Build and maintain end-to-end pipelines for feature engineering, training, evaluation, deployment, and monitoring, ensuring training–serving consistency

Improve latency, throughput, reliability, and observability of real-time and near–real-time inference systems operating at scale.

Translate product goals (conversion, retention, revenue, engagement) into clear modeling objectives and technical specification.

Collaborate closely with product managers, backend engineers, and infrastructure partners to ensure ML systems fit naturally into the existing architecture without introducing brittle dependencie

Develop evaluation frameworks that tie offline metrics to online A/B results, ensuring changes are measurable, interpretable, and aligned with product impact

Clearly communicate assumptions, trade-offs, risks, and technical constraints to both technical and non-technical stakeholders

Provide technical leadership for ML-driven decision systems, guiding the organization toward unified policy models and consistent action-selection frameworks across surfaces

Mentor engineers and scientists, setting a high bar for modeling rigor, production quality, experimentation discipline, and responsible ML

Shape long-term strategy for scalable, maintainable ML decisioning, bringing modern approaches—including sequential decisioning and RL-adjacent techniques—into production where appropriate

What you bring to the table

8+ years of applied ML or ML-heavy engineering experience, with a track record of shipping production models that drive measurable business impact

Deep expertise in classical ML techniques (e.g., boosted trees, GLMs, survival models, uplift modeling)

Experience with reinforcement learning, contextual bandits, or sequential decision-making

Strong engineering skills with Python and common ML frameworks (scikit-learn, PyTorch, XGBoost, LightGBM, etc.)

Demonstrated experience integrating ML systems into complex product architectures, ideally including monolithic applications

Experience defining integration boundaries, solving backend/ML interface issues, and collaborating with infra teams on serving patterns

Strong understanding of experimentation design, causal analysis, and the relationship between offline and online evaluation

Excellent communication skills for conveying technical constraints and integration trade-offs

A strong ownership mindset centered on reliability, maintainability, and long-term system health

Bonus points if you have:

Background in causal ML or uplift modeling

Experience with paywall optimization, monetization systems, or churn modeling

Knowledge of real-time inference architectures, feature stores, or streaming systems

Publications or open-source contributions in ML, RL, causal inference, or system integration

Compensation, Benefits & Perks:

Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps.  Total compensation for this role is market competitive, including a starting base salary of $190,000 - $274,500, depending on location and experience, as well as company stock options

Collaborate with your manager and team to create a healthy work-life balance

20 vacation days that we expect you to take!

Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice)

Employer-sponsored 401k plan with company match

Access to LinkedIn Learning and other resources to support professional growth

Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits

40 hours of annual paid time off to participate in volunteer programs of choice