Applied AI Engineer

3w3 weeks ago

Quizlet

New York City, US · Full-time · $178,000 – $330,000

About this role

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.

Our Applied AI team’s 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, 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.

You’ll work at the forefront of Quizlet’s AI strategy in Personalization & Ranking or Generative AI & Agentic Systems. Build a variety of models from Two-Tower retrieval and multi-task rankers to RAG/LLM pipelines. Ensure robust evaluation and responsible deployment of systems.

Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential. Deliver an AI-driven learning coach recognized as best-in-class. This onsite role requires a minimum of three days per week in the office: Monday, Wednesday, and Thursday.

Requirements

Experience building retrieval and ranking systems like Two-Tower retrieval and multi-task rankers
Proficiency implementing LLM pipelines including RAG, instruction-tuning (SFT/DPO), and prompt optimization
Familiarity with evaluation metrics such as NDCG, AUC for ranking and BLEU, BERTScore for generative systems
Knowledge of ML deployment practices including training-serving consistency, drift detection, and canarying/rollback
Background in personalization systems matching learners with content, experiences, and monetization
Experience with GenAI and agentic systems for tutoring, content synthesis, and productivity tools
Ability to connect AI modeling to business metrics like engaged learners, retention, and revenue

Responsibilities

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 rotations