Senior Machine Learning Engineer, Personalization and Recommendations

Quizlet-posted 20 days ago
Full-time • Senior
Onsite • San Francisco, CA
251-500 employees
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As a Senior Machine Learning Engineer on the Personalization & Recommendations team, you will design, build, and optimize large-scale retrieval, ranking and recommendation systems that directly shape how learners discover and engage with Quizlet. You’ll bring strong expertise in modern recommender systems — from deep learning–based retrieval and embeddings to multi-task ranking and evaluation — and contribute to the evolution of Quizlet’s personalization capabilities. Additionally, you will work at the intersection of machine learning, product, 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 models, deep ranking networks, and ANN-based vector search for real-time personalization
  • Build and maintain model training, evaluation, and deployment pipelines, ensuring reliability, training–serving consistency, observability, and robust monitoring
  • Partner with Product and Data Science to translate learner objectives (engagement, retention, mastery) into measurable modeling goals and experiment designs
  • Advance evaluation methodologies, contributing to offline metric design (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 in production environments
  • Stay informed on industry and research trends, evaluating opportunities to meaningfully apply them within Quizlet’s ecosystem.
  • Mentor junior and mid-level engineers, supporting technical growth, experimentation rigor, and responsible ML practices
  • Champion collaboration, inclusion, curiosity, and data-driven problem solving, contributing to a healthy and productive team culture
  • 5+ years of experience in applied machine learning or ML-heavy software engineering, with a strong focus on personalization, ranking, or recommendation systems
  • Demonstrated impact improving key 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, such as Two-Tower models, deep cross networks, Transformers, or MMoE, and the ability to apply them to real-world problems
  • Experience with large-scale embedding models and vector search, including FAISS, ScaNN, or similar systems.
  • Proficiency in experiment design and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B test outcomes to drive product decisions
  • Clear, effective communication, collaborating well with product managers, data scientists, engineers, and cross-functional partners
  • A growth and mentorship mindset, helping elevate team quality in modeling, experimentation, and reliability.
  • Commitment to responsible and inclusive personalization, ensuring our systems respect learner privacy, fairness, and diverse goals
  • Publications or open-source contributions in RecSys, search, or ranking
  • Familiarity with reinforcement learning for recommendations or contextual bandits
  • Experience with hybrid RecSys systems blending collaborative filtering, content understanding, and LLM-based reasoning
  • Prior work in consumer or EdTech applications with personalization at scale
  • 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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