Sr. Staff Applied AI Engineer bei Quizlet-2

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).- Generative AI & Agentic Systems – LLM‑powered tutoring, content understanding/synthesis, and tools that boost learner outcomes and creator productivity.You will architect and ship 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, and mentor senior engineers to multiply impact across the org.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.

In this role, you will:

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 outcomes

What 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 ambiguity

Bonus 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

40 hours of annual paid time off to participate in volunteer programs of choice

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