About the role
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 (Applied AI): Our mission is to invent and deploy the next generation of intelligent, personalized, and adaptive learning experiences. We’re consolidating AI efforts across the company into a unified portfolio and are accountable for a disproportionate share of Quizlet’s growth and product differentiation. You’ll partner closely with Product, Data Science, and the AI & Data Platform to deliver an AI‑driven learning coach that’s recognized as best‑in‑class.
About
the Role: We are looking for Applied Ai Engineers ranging from the Senior to Staff as well as Sr. Staff levels (note: leveling decisions made through the interview process). You’ll be working at the forefront of our AI strategy, shaping Quizlet’s AI development in one of the two complementary domains: Personalization & Ranking – retrieval and ranking systems that match learners with the right content, experiences, and monetization moments across surfaces (search, feed, notifications, ads). Generative AI & Agentic Systems – LLM‑powered tutoring, content understanding/synthesis, and tools that boost learner outcomes and creator productivity. You will work on a variety of models and modeling systems (from Two‑Tower retrieval and multi‑task rankers to RAG/LLM pipelines), ensure robust evaluation, and responsible deployment We’re happy to share that this is an onsite position in either our Denver, San Francisco, Seattle, or NYC. 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. \n In this role, you will: Contribute to the technical roadmap for applied AI across personalization, ranking, search, recommendations, and GenAI/LLM systems; help connect modeling work to business metrics (engaged learners, conversion, retention, revenue) Build components of end‑to‑end ML systems: candidate sourcing, embedding platforms & ANN retrieval, multi‑stage ranking (early/late), and value modeling with guardrails for fairness and integrity Implement LLM‑based features: build RAG pipelines, apply instruction‑/preference‑tuning techniques (e.g., SFT/DPO), optimize prompts, and improve latency/cost‑aware inference; contribute to offline evals + human‑in‑the‑loop and online success metrics Help develop "Learner 360" representations by working with behavior signals, explicit inputs, and conversational context to create robust embeddings reused across surfaces Support evaluation infrastructure: contribute to the eval harness for both ranking and generative systems (offline metrics like NDCG/AUC/BLEU/BERTScore; quality/safety scorecards), and help close the loop with online A/B experiments Ship reliable systems at scale: ensure training‑serving consistency, implement drift detection, follow canarying/rollback protocols, participate in on‑call rotation for model services, and maintain strong CI/CD for features & models Collaborate with and learn from senior ML/SWE teammates; write high‑quality code and follow best practices for experimentation rigor and reproducibility Work closely with Product, Design, Legal, and Data Science on objectives, tradeoffs, and responsible AI practices Stay current with ML research (RecSys, LLMs, multimodal) and propose new methods that could improve learner outcomes What you bring to the table: 6+ years of industry experience in applied ML/AI or ML‑heavy software engineering BS/MS in CS, ML, or related quantitative field (or equivalent experience) Experience building ranking/personalization or search systems (retrieval, Two‑Tower/dual encoders, multi‑task rankers) and contributing to online metric improvements (e.g., CTR, session depth, retention) Hands‑on experience with LLM/GenAI systems: data curation, fine‑tuning (SFT/PEFT, preference optimization), prompt engineering, evaluation, and productionization considerations (latency/cost/safety) Strong skills in Python/PyTorch, data and feature engineering, distributed training/inference on GPUs, and familiarity with modern MLOps (model registry, feature stores, monitoring, drift) Solid experiment design (offline/online), metrics literacy, and ability to translate product goals into modeling solutions Strong collaboration skills and eagerness to learn from senior engineers; some experience mentoring junior teammates is a plus