Machine Learning Engineer, Monetization & Decision Systems

Quizlet • Denver, CO

1d • Onsite

About The Position

Quizlet is seeking Machine Learning Engineers, ranging from Senior to Staff levels, to join their Monetization & Decision Systems team. This role is responsible for developing and integrating predictive and decisioning models that drive key business metrics such as monetization, retention, and activation. The engineer will lead both modeling efforts and technical integration, ensuring seamless incorporation of complex ML systems into Quizlet's product architecture. This involves designing models like conversion propensity, churn risk, LTV, and sequential decisioning, and collaborating with product and infrastructure teams to ensure reliable, efficient, and maintainable deployment at scale. A significant part of the role includes identifying dependencies, defining integration contracts, and shaping technical solutions for ML-driven decisioning. The position is based in Denver, San Francisco, Seattle, or NYC, with a requirement to be in the office a minimum of three days per week (Monday, Wednesday, and Thursday).

Requirements

• 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.

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.
• 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.

Benefits

• Company stock options
• Healthy work-life balance
• 20 vacation days
• 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
• 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: Senior
• Education Level: No Education Listed