About the role
Quizlet - Machine Learning Engineer, Personalization and Recommendations Machine Learning Engineer, Personalization and Recommendations San Francisco, CA / Denver, CO / New York, NY; Seattle, WA Engineering – AI and Data / Full-time / Hybrid apply for this job About Quizlet: At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. We’re a $1B+ learning platform used by two-thirds of U.S. high school students and half of college students, powering over 1 billion learning interactions each week. 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 Learning Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.
About
the Team: The Personalization & Recommendations team at Quizlet is building personalized learning experiences that help millions of learners study more effectively. We are looking for Machine Learning Engineers ranging from the Senior to Staff as well as Sr. Staff levels (note: leveling decisions made through the interview process). You’ll bring strong expertise in modern recommender systems — from deep learning–based retrieval and embeddings to multi-stage ranking and evaluation — and contribute to the evolution of Quizlet’s personalization capabilities. You’ll work at the intersection of machine learning, product, and scalable systems, ensuring our recommendations are performant, responsible, and aligned with learner outcomes, privacy, and fairness. We’re happy to share that this is an onsite position . 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 this work environment enhances efficiency, fosters collaboration, and supports growth for both employees and the organization. In this role, you will: Design and implement personalization models across candidate retrieval, ranking, and post-ranking layers, leveraging user embeddings, contextual signals, and content features. Develop scalable retrieval and serving systems using architectures such as Two-Tower models, deep ranking networks, and ANN-based vector search for real-time personalization. Build and maintain model training, evaluation, and deployment pipelines, ensuring reliability, training-serving consistency, observability, and robust monitoring. Partner with Product and Data Science to translate learner objectives such as engagement, retention, and mastery into measurable modeling goals and experiment designs. Advance evaluation methodologies, contributing to offline metric design such as NDCG, CTR, AUC, and calibration, and supporting rigorous A/B testing to measure learner and business impact. Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and serving cost in production environments. Stay informed on industry and research trends, evaluating opportunities to meaningfully apply them within Quizlet’s ecosystem. Mentor engineers, supporting technical growth, experimentation rigor, and responsible ML practices. Champion collaboration, inclusion, curiosity, and data-driven problem solving, contributing to a healthy and productive team culture. Depending on level, contribute to broader technical strategy, guide architectural decisions, and influence personalization direction across teams and product surfaces. What you bring to the table: Minimum 5+ years of experience in applied machine learning or ML-heavy software engineering, with a strong focus on personalization, ranking, or recommendation systems. Demonstrated impact improving key metrics such as CTR, retention, engagement, or other learner-facing outcomes through recommender or search systems in production. Strong hands-on skills in Python and PyTorch, with expertise in data and feature engineering, distributed training and inference on GPUs, and familiarity with modern MLOps practices, including model registries, feature stores, monitoring, and drift detection. Deep understanding of retrieval and ranking architectures, such as Two-Tower models, deep cross networks, Transformers, MMoE, or similar approaches, and the ability to apply them to real-world problems. Experience with large-scale embedding models and vector search systems, including FAISS, ScaNN, or similar technologies. Proficiency in experiment design and evaluation, connecting offline metrics such as AUC, NDCG, and calibration with online A/B test outcomes to drive product decisions. Clear, effective communication, with the ability to collaborate well with product managers, data scientists, engineers, and cross-functional partners. A growth and mentorship mindset, helping elevate team quality in modeling, experimentation, and reliability. Commitment to responsible and inclusive personalization, ensuring our systems respect learner privacy, fairness, and diverse goals. Additional strengths for Senior Staff candidates: Experience shaping technical strategy across teams or disciplines, balancing long-term architectural vision with near-term product and business priorities. Demonstrated leadership through influence, including aligning stakeholders, guiding teams through ambiguity, and driving accountability for outcomes. Ability to communicate complex technical trade-offs clearly to senior leadership and cross-functional audiences. Experience mentoring senior engineers or applied scientists and helping raise the technical bar across an organization. Proven ability to lead large, ambiguous initiatives and influence platform, product, and modeling direction at scale.