Senior Staff Applied AI Engineer, Quizlet

• 10+ years of industry experience in applied ML/AI or ML‑heavy software engineering, including staff‑level impact leading cross‑functional efforts end‑to‑end
• BS/MS/PhD in CS, ML, or related quantitative field (or equivalent experience)
• Proven record shipping large‑scale ranking/personalization or search systems (retrieval, Two‑Tower/dual encoders, multi‑task rankers), and improving online metrics (e.g., CTR, session depth, retention)
• Hands‑on with LLM/GenAI systems: data curation, fine‑tuning (SFT/PEFT, preference optimization), prompt engineering, evaluation, and productionization (latency/cost/safety)
• Deep fluency in Python/PyTorch, data and feature engineering, distributed training/inference on GPUs, and modern MLOps (model registry, feature stores, monitoring, drift)
• Strong experiment design (offline/online), metrics literacy, and skills translating ambiguous product goals into tractable modeling roadmaps
• Demonstrated technical leadership: mentoring senior engineers, setting architecture, and driving consensus amid ambiguity
• At Quizlet, we’re excited about passionate people joining our team—even if you don’t check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute

Desirable

• 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 Sr. Staff Applied AI Engineer, you will be the hands‑on technical leader shaping Quizlet’s AI develop 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
• 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 (RecSys, LLMs, multimodal) and selectively introduce methods that measurably improve learner outcomes

• We work to create a holistic interview process, where both Quizlet and candidates have an opportunity to view what it would be like to work together, in exploring a mutually beneficial partnership
• We provide a transparent setting that gives a comprehensive view of who we are!