Senior Machine Learning Engineer, Personalization and 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 mission 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 Machine Learning Engineer on the Personalization & Recommendations team, you will design, build, and optimize large-scale retrieval, ranking and recommendation systems that directly shape how learners discover and engage with Quizlet.
You’ll bring strong expertise in modern recommender systems — from deep learning–based retrieval and embeddings to multi-task ranking and evaluation — and contribute to the evolution of Quizlet’s personalization capabilities.
Additionally, you will 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 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.

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 (engagement, retention, mastery) into measurable modeling goals and experiment designs
• Advance evaluation methodologies, contributing to offline metric design (e.g., NDCG, CTR, 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 junior and mid-level 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

What you bring to the table:

• 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, or engagement 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, or MMoE, and the ability to apply them to real-world problems
• Experience with large-scale embedding models and vector search, inclu