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Staff Machine Learning Engineer

Quizlet (View all Jobs)

San Francisco, CA

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I'm a one-man team looking to improve tech interviews, and could use any support! 😄

Interview Process

1. Preliminary screen 2. Role-specific applied exercises where candidates create new projects/apps or make improvements to pre-existing code

Salary

$209,920 - $285,000

Programming Languages Mentioned

Python

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 Personalization & 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.
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 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
    • What you bring to the table:
  • 10+ 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
    • Bonus points if you have:
  • 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
    • Compensation, Benefits & Perks:
  • Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps.  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
    • Why Join Quizlet?

     🌎 Massive reach: 60M+ users, 1B+ interactions per week
     🧠 Cutting-edge tech: Generative AI, adaptive learning, cognitive science
     📈 Strong momentum: Top-tier investors, sustainable business, real traction
     🎯 Mission-first: Work that makes a difference in people’s lives
     🤝 Inclusive culture: Committed to equity, diversity, and belonging


    We strive to make everyone feel comfortable and welcome!
    We work to create a holistic interview process, where both Quizlet and candidates have an opportunity to view what it would be like to work together, in exploring a mutually beneficial partnership.

    We provide a transparent setting that gives a comprehensive view of who we are!  

    In Closing:

    At Quizlet, we’re excited about passionate people joining our team—even if you don’t check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute. Our culture is all about taking initiative, learning through challenges, and striving for high-quality work while staying curious and open to new ideas. We believe in honest, respectful communication, thoughtful collaboration, and creating a supportive space where everyone can grow and succeed together.”

    Quizlet’s success as an online learning community depends on a strong commitment to diversity, equity, and inclusion. 

    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!

    To All Recruiters and Placement Agencies:

    At this time, Quizlet does not accept unsolicited agency resumes and/or profiles. 
    Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third-party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet.
    #LI-FT

    Please mention No Whiteboard if you apply!
    I'm a one-man team looking to improve tech interviews, and could use any support! 😄

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