Applied AI Engineer
About The Position
Quizlet is seeking Applied AI Engineers at Senior to Staff, and Sr. Staff levels to join their Applied AI team. This team is responsible for inventing and deploying intelligent, personalized, and adaptive learning experiences, and is a key driver of Quizlet's growth and product differentiation. The role involves working on AI strategy, shaping AI development in either Personalization & Ranking or Generative AI & Agentic Systems. You will collaborate with Product, Data Science, and the AI & Data Platform to deliver a best-in-class AI-driven learning coach. The position is onsite in Denver, San Francisco, Seattle, or NYC, with a requirement of being in the office a minimum of three days per week (Monday, Wednesday, and Thursday), plus additional days as needed by management.
Requirements
- 6+ years of industry experience in applied ML/AI or ML‑heavy software engineering
- BS/MS in CS, ML, or related quantitative field (or equivalent experience)
- Experience building ranking/personalization or search systems (retrieval, Two‑Tower/dual encoders, multi‑task rankers) and contributing to online metric improvements (e.g., CTR, session depth, retention)
- Hands‑on experience with LLM/GenAI systems: data curation, fine‑tuning (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
Nice To Haves
- Some experience mentoring junior teammates is a plus
- 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)
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 rotation for model services, and maintain strong CI/CD for features & models
- Collaborate with and learn from senior ML/SWE teammates; write high‑quality 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
Benefits
- Company stock options
- 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
- Wellness benefits
- 40 hours of annual paid time off to participate in volunteer programs of choice
- 20 vacation days
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Associate degree
