Quizlet is looking to design and deliver AI-powered learning tools that scale across the world and unlock human potential by building the core intelligence behind how Quizlet matches learners with content, activities and experiences that best fit their goals.
Requirements
- 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, including FAISS, ScaNN, or similar systems.
- Proficiency in experiment design and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B test outcomes to drive product decisions
- Publications or open-source contributions in RecSys, search, or ranking
Responsibilities
- 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.
Other
- 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.
- 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
- Clear, effective communication, collaborating well with product managers, data scientists, engineers, and cross-functional partners
