Senior Staff Machine Learning Engineer, Personalization & Recommendations

About Quizlet:
At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our $1B+ 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.
We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We’re energized by the potential to power more learners through multiple approaches and various tools.
Let’s Build the Future of LearningJoin us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.
About the Team:
The Personalization & Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content, activities, and experiences that best fit their goals. We power recommendation and search systems across multiple surfaces, from home feed and search results to adaptive study modes.
Our team's objective is to make Quizlet feel uniquely tailored for every learner by combining cutting-edge machine learning, scalable infrastructure, and insights from learning science.
You’ll collaborate closely with Product Managers, Data Scientists, Platform Engineers, and fellow ML engineers to deliver personalized learning pathways that drive engagement, satisfaction, and measurable learning outcomes.
About the Role:
As a senior technical leader on the Personalization & Recommendations team, you’ll not only architect cutting-edge personalization systems but also guide the strategic direction of Quizlet’s AI-driven learner experience, mentoring peers and influencing decisions across the company. In this role, you’ll architect and implement large-scale retrieval, ranking and recommendation systems that directly shape the learner experience. You’ll bring modern RecSys expertise (from deep learning–based retrieval and embeddings to multi-task ranking and reinforcement learning) and help evolve Quizlet’s personalization stack.
You’ll help define and deliver systems that learn from billions of interactions while respecting learner privacy, fairness and 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.

In this role, you will:

• Work closely with other senior leaders to define and drive the long-term technical vision for personalization and recommendations across multiple Quizlet surfaces, ensuring alignment between modeling strategy, platform capabilities, and product roadmaps
• Communicate complex modeling trade-offs and recommendations to diverse audiences (from senior leadership to cross-functional partners) influencing decisions through clear reasoning, data, and empathy
• Architect and build large-scale personalization models across candidate retrieval, ranking, and post-ranking layers, leveraging user embeddings, contextual signals, and content features to power adaptive learning experiences
• Develop scalable retrieval and serving systems using modern architectures such as Two-Tower, deep ranking, and ANN-based vector search for real-time personalization at global scale
• Lead model training, evaluation, and deployment pipelines for retrieval and ranking systems, ensuring training-serving consistency, reliability, and robust monitoring
• Partner closely with Product and Data Science to translate learning objectives (e.g., engagement, retention, and mastery) into measurable modeling goals and experimentation frameworks
• Advance evaluation methodologies by refining offline metrics (e.g., NDCG, CTR, calibration) and online A/B testing to rigorously measure learner impact and model performance
• Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and cost-efficient serving in production environments
• Stay at the forefront of personalization and RecSys research, bringing relevant advances from top conferences (KDD, WSDM, SIGIR, RecSys, NeurIPS) into applied production systems
• Mentor and coach engineers and applied scientists, fostering technical excellence, reproducibility, and responsible AI practices across the organization
• Champion a culture of collaboration, inclusivity, and experimentation, helping elevate Quizlet’s AI craft and ensuring personalization systems serve learners equitably and effectively