Senior/Staff Applied AI Engineer

Quizlet • San Francisco, CA

34d • $205,000 - $285,000 • Onsite

About The Position

As an Applied AI Engineer, you will be working at the forefront of our AI strategy, 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 work on 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 We're happy to share that this is an onsite position in our San Francisco office. 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.

Requirements

• 5-8+ 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; some experience mentoring junior teammates is a plus

Nice To Haves

• 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

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Educational Services
• Number of Employees: 251-500 employees