About Quizlet:
Quizlet’s mission is to help every learner reach their goals in an effective and enjoyable way. Our learning platform, valued at over $1B, supports tens of millions of students each month—including two-thirds of U.S. high schoolers and half of U.S. college students—driving more than 2 billion study interactions monthly.
We combine cognitive science with machine learning to personalize and improve learning for students, professionals, and lifelong learners. We’re excited about expanding our impact through multiple methods and tools.

Let’s Build the Future of Learning
Join us in creating and launching AI-powered learning tools that scale globally and help unlock human potential.

About the Team:
The Personalization & Recommendations ML Engineering team develops the intelligence that connects learners with the content, activities, and experiences most relevant to their goals. We support recommendation and search systems across the platform, from home feed suggestions to adaptive study features.
Our mission is to make Quizlet feel customized for every learner through advanced machine learning, scalable systems, and insights from learning science. You’ll work closely with Product Managers, Data Scientists, Platform Engineers, and ML engineers to deliver personalized pathways that boost engagement, satisfaction, and learning results.

About the Role:
As a senior technical leader on the Personalization & Recommendations team, you’ll shape the architecture of advanced personalization systems and guide the strategic vision behind Quizlet’s AI-powered experiences. You’ll mentor peers and influence decisions across the company.
You will design and deploy large-scale retrieval, ranking, and recommendation systems that directly affect how learners engage with Quizlet. You’ll apply modern RecSys methods—from deep-learning retrieval and embeddings to multi-task ranking and reinforcement learning—to advance our personalization stack.
You’ll help build systems that learn from billions of interactions while upholding privacy, fairness, and integrity.
This is an onsite role based in our San Francisco office. To support effective collaboration, employees are expected to be in the office at least three days a week: Monday, Wednesday, and Thursday, plus any additional days as needed.

In this role, you will:
• Partner with senior leaders to define and steer the long-term technical strategy for personalization and recommendations across the platform
• Explain complex modeling choices clearly to both technical and non-technical audiences, influencing decisions with data and thoughtful reasoning
• Architect and implement large-scale personalization models across retrieval, ranking, and post-ranking stages, using embeddings, contextual signals, and content features
• Build scalable retrieval and serving systems using modern architectures such as Two-Tower models, deep ranking methods, and ANN-based vector search for real-time personalization
• Lead training, evaluation, and deployment pipelines, ensuring consistency, reliability, and strong monitoring
• Translate learning goals (engagement, retention, mastery) into modeling targets and experimental frameworks in partnership with Product and Data Science
• Improve evaluation methods by refining offline metrics (NDCG, CTR, calibration) and strengthening A/B testing strategies
• Work with infrastructure teams to optimize distributed training, inference latency, and cost-efficient serving
• Stay current with advancements in personalization and recommendation research and incorporate relevant findings from leading conferences
• Mentor engineers and applied scientists, promoting technical excellence, reproducibility, and responsible AI practices
• Foster a collaborative and inclusive team culture while ensuring personalization systems serve learners fairly and effectively

What you bring:
• 12+ years of experience in applied ML or ML-heavy engineering with deep knowledge of personalization, ranking, or recommendation systems
• Proven ability to guide technical strategy across teams while balancing long-term goals with short-term needs
• Outstanding communication and storytelling skills for conveying complex ideas clearly to varied audiences
• Demonstrated leadership through influence and the ability to align teams around shared, measurable outcomes
• Experience mentoring senior engineers and applied scientists and driving cross-team technical initiatives
• A record of improving key online metrics (CTR, retention, engagement) through production recommendation or search systems
• Strong understanding of modern retrieval and ranking architectures (Two-Tower, deep cross networks, GNNs, MMoE, Transformers) and multi-stage RecSys pipelines
• Hands-on experience with Python, PyTorch, feature engineering, distributed GPU training and inference, and MLOps tools (model registries, feature stores, monitoring, drift detection)
• Expertise in large-scale embedding models and vector search systems (FAISS, ScaNN, etc.)
• Strong experimentation skills, connecting offline metrics (AUC, NDCG, calibration) with online results to inform decisions
• Commitment to collaboration, inclusion, constructive debate, and shared ownership

Bonus points for:
• Publications or open-source work in RecSys, search, or ranking
• Experience with reinforcement learning or contextual bandits for recommendations
• Work with hybrid RecSys approaches mixing collaborative filtering, content understanding, and LLM reasoning
• Background in consumer or EdTech personalization at scale

Compensation, Benefits & Perks:
• Quizlet is an equal opportunity employer committed to an inclusive work environment. We support salary transparency to reduce bias. Total compensation is competitive and includes a base salary of $242,240–$344,000 (based on location and experience) plus stock options
• Healthy work-life balance supported by your manager and team
• 20 days of vacation
• Competitive medical, dental, and vision coverage
• 401k with company match
• Access to LinkedIn Learning and other growth resources
• Paid Family Leave, FSA, HSA, commuter, and wellness benefits
• 40 hours of annual paid volunteer time

Why Join Quizlet?
• Huge impact: 60M+ users and over 1B weekly interactions
• Cutting-edge tech: Generative AI, adaptive learning, cognitive science
• Strong momentum: Great investors, sustainable growth, real traction
• Mission-driven: Work that improves people’s lives
• Inclusive culture: We value equity, diversity, and belonging

We aim to ensure every candidate feels welcomed and respected. Our interview process is designed to help both you and Quizlet understand what working together would look like.
We offer transparency and an honest view of who we are.

In Closing:
We’re excited about candidates who are passionate and curious—even if you don’t meet every requirement. We value diverse perspectives and believe everyone has something important to contribute. Our culture emphasizes initiative, learning from challenges, high-quality work, and open-minded collaboration. We focus on respectful communication and creating an environment where everyone can grow.
Quizlet’s success relies on our commitment to diversity, equity, and inclusion. We welcome applicants from all backgrounds, and we strongly encourage women, people of color, LGBTQ+ individuals, people with disabilities, and veterans to apply. Join us!

To Recruiters and Agencies:
Quizlet does not accept unsolicited resumes. Please do not send them to our website or employees. We will not pay any associated fees, and unsolicited submissions will be considered Quizlet property.