Applied AI Engineer
San Francisco, CA - USA
Job Summary
About the Team (Applied AI):
Our mission is to invent and deploy the next generation of intelligent personalized and adaptive learning experiences. Were consolidating AI efforts across the company into a unified portfolio and are accountable for a disproportionate share of Quizlets growth and product differentiation. Youll partner closely with Product Data Science and the AI & Data Platform to deliver an AIdriven learning coach thats recognized as bestinclass.
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).
Youll be working at the forefront of our AI strategy shaping Quizlets 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 LLMpowered 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 TwoTower retrieval and multitask rankers to RAG/LLM pipelines) ensure robust evaluation and responsible deployment
Were 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.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 endtoend ML systems: candidate sourcing embedding platforms & ANN retrieval multistage ranking (early/late) and value modeling with guardrails for fairness and integrity
- Implement LLMbased features: build RAG pipelines apply instruction/preferencetuning techniques (e.g. SFT/DPO) optimize prompts and improve latency/costaware inference; contribute to offline evals humanintheloop 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 trainingserving consistency implement drift detection follow canarying/rollback protocols participate in oncall rotation for model services and maintain strong CI/CD for features & models
- Collaborate with and learn from senior ML/SWE teammates; write highquality 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 MLheavy software engineering
- BS/MS in CS ML or related quantitative field (or equivalent experience)
- Experience building ranking/personalization or search systems (retrieval TwoTower/dual encoders multitask rankers) and contributing to online metric improvements (e.g. CTR session depth retention)
- Handson experience with LLM/GenAI systems: data curation finetuning (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
Bonus points if you have:
- EdTech or consumer mobile experience; conversational tutoring or learning scienceinformed modeling
- Publications/opensource 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 $178000 - $330000 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
- 40 hours of annual paid time off to participate in volunteer programs of choice
Required Experience:
IC
About Company
Quizlet makes simple learning tools that let you study anything. Start learning today with flashcards, games and learning tools — all for free.