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Job DescriptionAbout 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 LearningJoin 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: 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). • 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 outcomesWhat you bring to the table:, • 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 ambiguityBonus points if you have:, • EdTech or consumer mobile experience; conversational tutoring or learning science‑informed modeling, • Publications/open‑source with RecSys/LLMs (e.g., RecSys, KDD, NeurIPS, ICLR, ACL), or contributions to safety/guardrails tooling, • Experience building on a modern MLOps stack (feature mgmt, orchestration, streaming, online inference at scale)Compensation, Benefits & Perks:, • Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps. Total compensation for this role is market competitive, including a starting base salary of $282,000 - $344,000, depending on location and experience, as well as company stock options, • Collaborate with your manager and team to create a healthy work-life balance, • 20 vacation days that we expect you to take!, • Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice), • Employer-sponsored 401k plan with company match, • Access to LinkedIn Learning and other resources to support professional growth, • Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits We strive to make everyone feel comfortable and welcome!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! In Closing: 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. Our culture is all about taking initiative, learning through challenges, and striving for high-quality work while staying curious and open to new ideas. We believe in honest, respectful communication, thoughtful collaboration, and creating a supportive space where everyone can grow and succeed together.” Quizlet’s success as an online learning community depends on a strong commitment to diversity, equity, and inclusion. As an equal opportunity employer and a tech company committed to societal change, we welcome applicants from all backgrounds. Women, people of color, members of the LGBTQ+ community, individuals with disabilities, and veterans are strongly encouraged to apply. Come join us! To All Recruiters and Placement Agencies: At this time, Quizlet does not accept unsolicited agency resumes and/or profiles. Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third-party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.