Senior Applied AI Engineer

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 Learning. Join us to design and deliver AI‑powered learning tools that scale across the world and unlock human potential.

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.

As a Sr. Staff Applied AI Engineer, you will be the hands‑on technical leader 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 architect and ship 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, and mentor senior engineers to multiply impact across the org.

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 facilitates increased work efficiency, team partnership, and supports growth as an employee and organization.

• Own the technical roadmap for applied AI spanning personalization, ranking, search, recommendations, and GenAI/LLM systems; tie modeling work directly to business metrics (engaged learners, conversion, retention, revenue)
• Design 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
• Lead LLM‑based features: stand up RAG pipelines, instruction‑/preference‑tuning (e.g., SFT/DPO/RL‑style), prompt engineering, and latency/cost‑aware inference strategies; define offline evals + human‑in‑the‑loop and online success metrics
• Create a “Learner 360” representation by synthesizing behavior signals, explicit inputs, and conversational context into robust embeddings reused across surfaces
• Institutionalize evaluation: build an eval harness for both ranking and generative systems (offline metrics like NDCG/AUC/BLEU/BERTScore; quality/safety scorecards; inter‑rater reliability), and close the loop with online A/B experiments
• Ship reliably at scale: drive training‑serving consistency, drift detection, canarying, rollbacks, on‑call standards for model services, and strong CI/CD for features & models
• Mentor and uplevel a high‑performing group of ML/SWE peers; set crisp technical direction and raise the bar on code quality, experimentation rigor, and reproducibility
• Partner deeply with Product, Design, Legal, and Data Science on objectives, risk/benefit tradeoffs, and responsible AI practices
• Stay current with the state of the art (Rec Sys, LLMs, multimodal) and selectively introduce methods that measurably improve learner outcomes

• 10+ years of industry experience in applied ML/AI or ML‑heavy…