Machine Learning Engineer, Personalization and Recommendations

Quizlet • San Francisco, NY

1d • $175,000 - $330,000 • Onsite

About The Position

At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. We’re a $1B+ learning platform used by two-thirds of U.S. high school students and half of college students, powering over 1 billion learning interactions each week. We blend cognitive science with machine learning to personalize and enhance the learning experience for students, professionals, and lifelong learners alike. We’re energized by the potential to power more learners through multiple approaches and various tools. Let’s Build the Future of Learning Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential. About the Team: The Personalization & Recommendations team at Quizlet is building personalized learning experiences that help millions of learners study more effectively. We are looking for Machine Learning Engineers ranging from the Senior to Staff as well as Sr. Staff levels (note: leveling decisions made through the interview process). You’ll bring strong expertise in modern recommender systems — from deep learning–based retrieval and embeddings to multi-stage ranking and evaluation — and contribute to the evolution of Quizlet’s personalization capabilities. You’ll 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. We’re happy to share that this is an onsite position . 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 this work environment enhances efficiency, fosters collaboration, and supports growth for both employees and the organization.

Requirements

• Minimum 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, engagement, or other learner-facing outcomes 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, MMoE, or similar approaches, and the ability to apply them to real-world problems.
• Experience with large-scale embedding models and vector search systems, including FAISS, ScaNN, or similar technologies.
• Proficiency in experiment design and evaluation, connecting offline metrics such as AUC, NDCG, and calibration with online A/B test outcomes to drive product decisions.
• Clear, effective communication, with the ability to collaborate 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.

Nice To Haves

• 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.

Responsibilities

• 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 such as engagement, retention, and mastery into measurable modeling goals and experiment designs.
• Advance evaluation methodologies, contributing to offline metric design such as NDCG, CTR, AUC, and 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 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.
• Depending on level, contribute to broader technical strategy, guide architectural decisions, and influence personalization direction across teams and product surfaces.

Benefits

• Total compensation for this role is market competitive, including a base salary of $175,000 to $330,000 depending on location, level (Senior, Staff, or Senior Staff), 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed