Staff Machine Learning Engineer, Quizlet

• 8+ years of experience in applied machine learning or ML-heavy software engineering, with a strong focus on personalization, ranking, or recommendation systems
• Track record of measurable impact, improving key online metrics such as CTR, retention, or engagement through recommender or search systems in production
• 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
• Deep understanding of retrieval and ranking architectures, including Two-Tower models, deep cross networks, Transformers, or MMoE, and how to apply them in production contexts
• Experience with large-scale embedding models and vector search (e.g., FAISS, ScaNN), including training, serving, and optimization at scale
• Proficiency in experiment design and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B test results to drive product decisions
• Ability to communicate complex technical ideas clearly, collaborating effectively with product managers, data scientists, and engineers across teams
• Growth and mentorship mindset, contributing to team learning and helping raise the bar for modeling quality, experimentation, and reliability
• Commitment to responsible and inclusive personalization, ensuring our ML systems respect learner privacy, fairness, and diverse goals
• As an equal opportunity employer and a tech company committed to societal change, we welcome applicants from all backgrounds. Women, people of color, members of the LGBTQ+ community, individuals with disabilities, and veterans are strongly encouraged to apply. Come join us!

Desirable

• As a Senior or Staff Machine Learning Engineer on the Personalization & Recommendations team, you’ll design and build large-scale retrieval, ranking, and recommendation systems that directly shape how learners discover and engage with Quizlet
• You’ll bring deep expertise in modern recommender systems — from deep learning–based retrieval and embeddings to multi-task ranking and evaluation — and help evolve Quizlet’s personalization stack to power adaptive, effective learning experiences
• You’ll work at the intersection of machine learning, product design, and scalable systems, ensuring our recommendations are performant, responsible, and aligned with learner outcomes, privacy, and fairness
• Design and implement personalization models across candidate retrieval, ranking, and post-ranking layers, leveraging user embeddings, contextual signals, and content features
• Develop scalable retrieval and serving systems using architectures such as Two-Tower, deep ranking, and ANN-based vector search for real-time personalization across surfaces
• Build and maintain model training, evaluation, and deployment pipelines, ensuring reliability, training–serving consistency, and robust monitoring
• Partner closely with Product and Data Science to translate learner objectives (engagement, retention, mastery) into measurable modeling goals and experimentation plans
• Advance evaluation methodologies, refining offline metrics (e.g., NDCG, CTR, calibration) and supporting rigorous A/B testing to measure learner and business impact
• Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and serving cost at scale
• Contribute to the long-term technical vision for personalization and recommendations, aligning modeling strategy with Quizlet’s AI and product roadmaps
• Stay current with RecSys research and industry trends, bringing relevant advances from top conferences (KDD, WSDM, SIGIR, RecSys, NeurIPS) into production
• Mentor other engineers and applied scientists, fostering technical growth, experimentation rigor, and responsible ML practices
• Champion collaboration, inclusion, and curiosity, helping build a team culture that values diverse perspectives and data-driven problem-solving