Senior Staff Machine Learning Engineer, Quizlet

• 12+ years of experience in applied machine learning or ML-heavy engineering, with deep expertise in personalization, ranking, or recommendation systems
• Proven ability to shape technical direction across multiple teams or disciplines, balancing long-term architectural vision with near-term product and business priorities
• Exceptional communication and storytelling skills — able to distill complex technical concepts into clear narratives for executives, product partners, and non-technical audiences
• Demonstrated leadership through influence, guiding teams through ambiguity, aligning stakeholders around measurable goals, and ensuring accountability for impact
• Experience mentoring senior engineers and applied scientists, leading technical working groups, and driving cross-team innovation and standardization
• Track record of measurable impact, improving key online metrics such as CTR, retention, and engagement through recommender, ranking, or search systems in production
• Deep technical understanding of modern retrieval and ranking architectures (e.g., Two-Tower, deep cross networks, GNNs, MMoE, Transformers) and multi-stage RecSys pipelines
• Strong hands-on skills in Python and PyTorch, with expertise in data and feature engineering, distributed training and inference on GPUs, and familiarity with modern MLOps practices — including model registries, feature stores, monitoring, and drift detection
• Experience with large-scale embedding models and vector search systems (FAISS, ScaNN, or similar), including training, serving, and optimization at scale
• Expertise in experimentation and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B results to drive confident, data-informed decisions
• Commitment to collaboration and inclusion, fostering a culture that values diverse perspectives, constructive debate, and shared ownership of results
• Experience with hybrid RecSys systems blending collaborative filtering, content understanding, and LLM-based reasoning
• At Quizlet, we’re excited about passionate people joining our team—even if you don’t check every box on the requirements list

Desirable

• The Personalization & Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content, activities, and experiences that best fit their goals
• We power recommendation and search systems across multiple surfaces, from home feed and search results to adaptive study modes
• Our team's objective is to make Quizlet feel uniquely tailored for every learner by combining cutting-edge machine learning, scalable infrastructure, and insights from learning science
• You’ll collaborate closely with Product Managers, Data Scientists, Platform Engineers, and fellow ML engineers to deliver personalized learning pathways that drive engagement, satisfaction, and measurable learning outcomes
• As a senior technical leader on the Personalization & Recommendations team, you’ll not only architect cutting-edge personalization systems but also guide the strategic direction of Quizlet’s AI-driven learner experience, mentoring peers and influencing decisions across the company
• In this role, you’ll architect and implement large-scale retrieval, ranking and recommendation systems that directly shape the learner experience
• You’ll bring modern RecSys expertise (from deep learning–based retrieval and embeddings to multi-task ranking and reinforcement learning) and help evolve Quizlet’s personalization stack
• You’ll help define and deliver systems that learn from billions of interactions while respecting learner privacy, fairness and integrity
• Work closely with other senior leaders to define and drive the long-term technical vision for personalization and recommendations across multiple Quizlet surfaces, ensuring alignment between modeling strategy, platform capabilities, and product roadmaps
• Communicate complex modeling trade-offs and recommendations to diverse audiences (from senior leadership to cross-functional partners) influencing decisions through clear reasoning, data, and empathy
• Architect and build large-scale personalization models across candidate retrieval, ranking, and post-ranking layers, leveraging user embeddings, contextual signals, and content features to power adaptive learning experiences
• Develop scalable retrieval and serving systems using modern architectures such as Two-Tower, deep ranking, and ANN-based vector search for real-time personalization at global scale
• Lead model training, evaluation, and deployment pipelines for retrieval and ranking systems, ensuring training-serving consistency, reliability, and robust monitoring
• Partner closely with Product and Data Science to translate learning objectives (e.g., engagement, retention, and mastery) into measurable modeling goals and experimentation frameworks
• Advance evaluation methodologies by refining offline metrics (e.g., NDCG, CTR, calibration) and online A/B testing to rigorously measure learner impact and model performance
• Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and cost-efficient serving in production environments
• Stay at the forefront of personalization and RecSys research, bringing relevant advances from top conferences (KDD, WSDM, SIGIR, RecSys, NeurIPS) into applied production systems
• Mentor and coach engineers and applied scientists, fostering technical excellence, reproducibility, and responsible AI practices across the organization
• Champion a culture of collaboration, inclusivity, and experimentation, helping elevate Quizlet’s AI craft and ensuring personalization systems serve learners equitably and effectively