Staff Machine Learning Engineer, Monetization & Decision Systems

Quizlet • San Francisco, CA

16d • Onsite

About The Position

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.

Requirements

• 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

Nice To Haves

• 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

Responsibilities

• 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

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 251-500 employees