Staff Machine Learning Engineer, Monetization amp; Decision SystemsQuizlet • United States

Jetzt Bewerben

Staff Machine Learning Engineer, Monetization amp; Decision Systems

Quizlet

United States

United States

Jetzt Bewerben

Über

divh2Staff Machine Learning Engineer/h2pAt Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our 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./ppWe blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. Were energized by the potential to power more learners through multiple approaches and various tools./ppJoin us to design and deliver AI-powered learning tools that scale across the world and unlock human potential./ppThe 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./ppWithin 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 Quizlets product architecture./ppAs 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./ppA 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./ppYoull 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./ppThis 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./ppIn this role you will:/pulliLead 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./liliDesign 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/liliYou 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/liliThis 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/liliEvaluation approaches that connect offline modeling metrics to online experimental outcomes/liliApply 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/liliLead 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/liliIdentify upstream and downstream dependencies within the product codebase and data ecosystem, and proactively address integration risks/liliDefine and negotiate clean integration boundaries, including API contracts, data interfaces, decision schemas, and fallback strategies, in collaboration with product and infrastructure engineering/liliPartner closely with Infrastructure Engineering to design scalable, resilient, and observable model-serving paths that integrate with Quizlets application stack/liliEmbed model-driven decisioning logic into backend and product flows in ways that are maintainable, testable, and compatible with existing systems/liliBuild and maintain end-to-end pipelines for feature engineering, training, evaluation, deployment, and monitoring, ensuring trainingserving consistency/liliImprove latency, throughput, reliability, and observability of real-time and nearreal-time inference systems operating at scale./liliTranslate product goals (conversion, retention, revenue, engagement) into clear modeling objectives and technical specification./liliCollaborate closely with product managers, backend engineers, and infrastructure partners to ensure ML systems fit naturally into the existing architecture without introducing brittle dependencies/liliDevelop evaluation frameworks that tie offline metrics to online A/B results, ensuring changes are measurable, interpretable, and aligned with product impact/liliClearly communicate assumptions, trade-offs, risks, and technical constraints to both technical and non-technical stakeholders/liliProvide technical leadership for ML-driven decision systems, guiding the organization toward unified policy models and consistent action-selection frameworks across surfaces/liliMentor engineers and scientists, setting a high bar for modeling rigor, production quality, experimentation discipline, and responsible ML/liliShape long-term strategy for scalable, maintainable ML decisioning, bringing modern approachesincluding sequential decisioning and RL-adjacent techniquesinto production where appropriate/li/ulpWhat you bring to the table:/pulli8+ years of applied ML or ML-heavy engineering experience, with a track record of shipping production models that drive measurable business impact/liliDeep expertise in classical ML techniques (e.g., boosted trees, GLMs, survival models, uplift modeling)/liliExperience with reinforcement learning, contextual bandits, or sequential decision-making/liliStrong engineering skills with Python and common ML frameworks (scikit-learn, PyTorch, XGBoost, LightGBM, etc.)/liliDemonstrated experience integrating ML systems into complex product architectures, ideally including monolithic applications/liliExperience defining integration boundaries, solving backend/ML interface issues, and collaborating with infra teams on serving patterns/liliStrong understanding of experimentation design, causal analysis, and the relationship between offline and online evaluation/liliExcellent communication skills for conveying technical constraints and integration trade-offs/liliA strong ownership mindset centered on reliability, maintainability, and long-term system health/li/ulpBonus points if you have:/pulliBackground in causal ML or uplift modeling/liliExperience with paywall optimization, monetization systems, or churn modeling/liliKnowledge of real-time inference architectures, feature stores, or streaming systems/liliPublications or open-source contributions in ML, RL, causal inference, or system integration/li/ulpCompensation, Benefits Perks:/pulliQuizlet 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/liliCollaborate with your manager and team to create a healthy work-life balance/lili20 vacation days that we expect you to take!/liliCompetitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice)/liliEmployer-sponsored 401k plan with company match/liliAccess to LinkedIn Learning and other resources to support professional growth/liliPaid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits/lili40 hours of annual paid time off to participate in volunteer programs of choice/li/ulpWhy Join Quizlet?/ppMassive reach: 60M+ users, 1B+ interactions per week/ppCutting-edge tech: Generative AI, adaptive learning, cognitive science/ppStrong/p/div

United States

Sprachkenntnisse

English

Hinweis für Nutzer

Dieses Stellenangebot stammt von einer Partnerplattform von TieTalent. Klicken Sie auf „Jetzt Bewerben“, um Ihre Bewerbung direkt auf deren Website einzureichen.

Jetzt Bewerben