Staff Machine Learning Engineer, Personalization & Recommendations

Quizlet • San Francisco, CA

13d • $209,920 - $285,000 • Onsite

About The Position

As a 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. 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.

Requirements

• 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

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, 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

Benefits

• Total compensation for this role is market competitive, including a starting base salary of $209,920 - $285,000, 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 251-500 employees