Quizlet · 1 hour ago
Staff Machine Learning Engineer, Monetization & Decision Systems
San Francisco, CA
Full-time
Hybrid
Lead/Staff
$190K/yr - $274K/yr
8+ years expQuizlet is a leading learning platform dedicated to helping learners achieve their outcomes through effective and delightful experiences. They are seeking a Staff Machine Learning Engineer to lead the development of predictive and decision-making models that drive monetization, retention, and personalized study guidance, collaborating closely with cross-functional teams to integrate these models into product workflows.
E-LearningEdTechEducationInternetKnowledge Management
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 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
Qualification
Required
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
Preferred
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
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
Company
Quizlet
Quizlet is a learning platform that uses activities and games to help students practice and master what they’re learning.
201-500 employees
H1B Sponsorship
Quizlet has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (12)
2024 (10)
2023 (7)
2022 (9)
2021 (1)
2020 (8)
Funding
Current Stage
Growth StageTotal Funding
$62MKey Investors
General AtlanticIcon Ventures
2020-05-13Series C· $30M
2018-02-06Series B· $20M
2015-11-23Series A· $12M
Leadership Team
Kurt Beidler
Chief Executive Officer
David Margulius
Board Member and Co-Founder
Recent News
2025-10-31
South Africa Today
2025-09-12
Mark Cuban Foundation
2025-09-09
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