Machine Learning Engineer, Monetization & Decision Systems

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.

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.

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

• 6+ 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 $174,000 - $330,000, 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