Machine Learning Engineer

Position: Staff Machine Learning Engineer
About Quizlet:

At Quizlet, our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our $1B+ learning platform serves tens of millions of students every month, including two-thirds of U.S. high schoolers and half of U.S. college students, powering over 2 billion learning interactions monthly.

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 Person alization & 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 mission 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.

About the Role:

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.

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 supports collaboration and accountability.

In this role, you will:

• 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 Rec Sys research and industry trends, bringing relevant advances from top conferences (KDD, WSDM, SIGIR, Rec Sys, 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

What you bring to the table:

• 10+ years of experience…