Why This Job is Featured on The SaaS Jobs
### Why this Role is Featured on The SaaS Jobs
Personalization and recommendations sit at the core of many scaled SaaS products, and this Senior Staff ML Engineering role is anchored in that reality: it targets production retrieval, ranking, and experimentation systems that influence what users see across multiple product surfaces. Quizlet’s scale—billions of interactions and a large active user base—places the work in the category of SaaS machine learning where modeling choices, latency, and serving architecture become product constraints, not academic exercises.
From a SaaS career standpoint, the role offers durable leverage: building end-to-end recommendation platforms, shaping evaluation standards, and translating product goals into measurable online impact are skills that travel across subscription businesses. The emphasis on training/serving consistency, monitoring, and cost-aware inference aligns with how modern SaaS teams operationalize ML, especially when personalization must remain reliable under continuous product iteration.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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 team's objective 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 technical leader on the Personalization & Recommendations team, you’ll not only architect cutting-edge personalization systems but also guide the strategic direction of Quizlet’s AI-driven learner experience, mentoring peers and influencing decisions across the company. In this role, you’ll architect and implement large-scale retrieval, ranking and recommendation systems that directly shape the learner experience. You’ll bring modern RecSys expertise (from deep learning–based retrieval and embeddings to multi-task ranking and reinforcement learning) and help evolve Quizlet’s personalization stack.
You’ll help define and deliver systems that learn from billions of interactions while respecting learner privacy, fairness and integrity.
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 facilitates increased work efficiency, team partnership, and supports growth as an employee and organization.
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In this role, you will:- Work closely with other senior leaders to define and drive the long-term technical vision for personalization and recommendations across multiple Quizlet surfaces, ensuring alignment between modeling strategy, platform capabilities, and product roadmaps
- Communicate complex modeling trade-offs and recommendations to diverse audiences (from senior leadership to cross-functional partners) influencing decisions through clear reasoning, data, and empathy
- Architect and build large-scale personalization models across candidate retrieval, ranking, and post-ranking layers, leveraging user embeddings, contextual signals, and content features to power adaptive learning experiences
- Develop scalable retrieval and serving systems using modern architectures such as Two-Tower, deep ranking, and ANN-based vector search for real-time personalization at global scale
- Lead model training, evaluation, and deployment pipelines for retrieval and ranking systems, ensuring training-serving consistency, reliability, and robust monitoring
- Partner closely with Product and Data Science to translate learning objectives (e.g., engagement, retention, and mastery) into measurable modeling goals and experimentation frameworks
- Advance evaluation methodologies by refining offline metrics (e.g., NDCG, CTR, calibration) and online A/B testing to rigorously measure learner impact and model performance
- Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and cost-efficient serving in production environments
- Stay at the forefront of personalization and RecSys research, bringing relevant advances from top conferences (KDD, WSDM, SIGIR, RecSys, NeurIPS) into applied production systems
- Mentor and coach engineers and applied scientists, fostering technical excellence, reproducibility, and responsible AI practices across the organization
- Champion a culture of collaboration, inclusivity, and experimentation, helping elevate Quizlet’s AI craft and ensuring personalization systems serve learners equitably and effectively
What you bring to the table:- 12+ years of experience in applied machine learning or ML-heavy engineering, with deep expertise in personalization, ranking, or recommendation systems
- Proven ability to shape technical direction across multiple teams or disciplines, balancing long-term architectural vision with near-term product and business priorities
- Exceptional communication and storytelling skills — able to distill complex technical concepts into clear narratives for executives, product partners, and non-technical audiences
- Demonstrated leadership through influence, guiding teams through ambiguity, aligning stakeholders around measurable goals, and ensuring accountability for impact
- Experience mentoring senior engineers and applied scientists, leading technical working groups, and driving cross-team innovation and standardization
- Track record of measurable impact, improving key online metrics such as CTR, retention, and engagement through recommender, ranking, or search systems in production
- Deep technical understanding of modern retrieval and ranking architectures (e.g., Two-Tower, deep cross networks, GNNs, MMoE, Transformers) and multi-stage RecSys pipelines.
- 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
- Experience with large-scale embedding models and vector search systems (FAISS, ScaNN, or similar), including training, serving, and optimization at scale
- Expertise in experimentation and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B results to drive confident, data-informed decisions
- Commitment to collaboration and inclusion, fostering a culture that values diverse perspectives, constructive debate, and shared ownership of results
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 $242,240 - $$344,000, 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
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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
