Staff Machine Learning Engineer, Monetization & Decision Systems
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Job Location:
San Francisco, CA - USA
Monthly Salary:
$ 190000 - 274500
Posted on:
7 hours ago
Vacancies:
1 Vacancy
Job Summary
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. Were energized by the potential to power more learners through multiple approaches and various tools.
Lets 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 user experiences that best fit their goals while also optimizing for business metrics that support long-term sustainability. We power recommendation and search systems across multiple surfaces such as the home feed search results and adaptive study modes as well as decision systems in ads and notifications that determine the timing and nature of key interventions.
Within this organization this role is responsible for the predictive and decisioning models that drive monetization retention activation and goal-aligned study guidance. These systems balance immediate impact with long-term user value and must integrate seamlessly into Quizlets product architecture.
As a Staff Machine Learning Engineer on the Personalization & Recommendations team you will lead both the modeling efforts and the technical integration work required to bring complex ML systems into production. This includes designing predictive and prescriptive models (such as conversion propensity churn risk LTV sequential decisioning and timing optimization) and collaborating closely with product and infrastructure engineering to ensure these models can be safely and cleanly embedded into existing product workflows.
A major part of this role involves identifying dependencies within the product codebase defining integration contracts with cross-functional partners and shaping technical solutions that allow ML-driven decisioning to operate reliably efficiently and maintainably at scale.
Youll work closely with product managers data scientists platform engineers backend engineers and fellow ML engineers to deliver ML-driven experiences that drive engagement satisfaction and measurable business outcomes.
About the Role:
As a Staff Machine Learning Engineer on the Personalization & Recommendations team you will lead the development of ML systems that decide what action Quizlet should take for a learner when that action should occur and under what constraints. This role focuses on action selection and policy design rather than content ranking alone and requires deep ownership of both modeling and production integration.
You will own the full lifecycle of these systems (from problem framing and model development to integration deployment and long-term reliability) working closely with product infrastructure and backend engineering partners. A core responsibility of this role is embedding model-driven decisions into Quizlets product in a way that is safe observable and maintainable including identifying dependencies defining clean interfaces and ensuring robust fallback behavior.
Your work will directly influence monetization retention activation and goal-aligned study guidance requiring you to balance short-term business impact with long-term learner value and product integrity.
Were 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.
In this role you will:
- Lead the design and development of predictive and prescriptive models (e.g. conversion propensity churn risk LTV uplift sequential decisioning and timing optimization) that drive learner-facing decisions across monetization lifecycle and study guidance surfaces.
- Design and build decisioning and policy models that determine learner-facing actions across product surfaces including monetization lifecycle and study guidance use cases. These systems operate under real-world product constraints and must optimize across multiple sometimes competing objectives
- You will work on problems such as: determining when and how to present paywalls discounts or value exchanges selecting personalized study modes or interventions based on learner state and intent triggering retention or churn-prevention actions at the right moment and balancing immediate conversion or revenue with long-term engagement and learning outcomes
- This role emphasizes: multi-objective optimization across monetization retention and user experience timing- and eligibility-aware decisioning rather than static predictions and consistent action selection across sessions and surfaces
- Evaluation approaches that connect offline modeling metrics to online experimental outcomes
- Apply advanced techniques such as uplift modeling survival analysis sequential decisioning and other policy-based approaches bringing them into production in collaboration with cross-functional partners
- Lead the end-to-end productionization of ML systems from modeling through integration ensuring models can be safely cleanly and reliably embedded into existing product workflows
- Identify upstream and downstream dependencies within the product codebase and data ecosystem and proactively address integration risks
- Define and negotiate clean integration boundaries including API contracts data interfaces decision schemas and fallback strategies in collaboration with product and infrastructure engineering
- Partner closely with Infrastructure Engineering to design scalable resilient and observable model-serving paths that integrate with Quizlets application stack
- Embed model-driven decisioning logic into backend and product flows in ways that are maintainable testable and compatible with existing systems
- Build and maintain end-to-end pipelines for feature engineering training evaluation deployment and monitoring ensuring trainingserving consistency
- Improve latency throughput reliability and observability of real-time and nearreal-time inference systems operating at scale.
- Translate product goals (conversion retention revenue engagement) into clear modeling objectives and technical specification.
- Collaborate closely with product managers backend engineers and infrastructure partners to ensure ML systems fit naturally into the existing architecture without introducing brittle dependencie
- Develop evaluation frameworks that tie offline metrics to online A/B results ensuring changes are measurable interpretable and aligned with product impact
- Clearly communicate assumptions trade-offs risks and technical constraints to both technical and non-technical stakeholders
- Provide technical leadership for ML-driven decision systems guiding the organization toward unified policy models and consistent action-selection frameworks across surfaces
- Mentor engineers and scientists setting a high bar for modeling rigor production quality experimentation discipline and responsible ML
- Shape long-term strategy for scalable maintainable ML decisioning bringing modern approachesincluding sequential decisioning and RL-adjacent techniquesinto production where appropriate
What you bring to the table
- 8 years of applied ML or ML-heavy engineering experience with a track record of shipping production models that drive measurable business impact
- Deep expertise in classical ML techniques (e.g. boosted trees GLMs survival models uplift modeling)
- Experience with reinforcement learning contextual bandits or sequential decision-making
- Strong engineering skills with Python and common ML frameworks (scikit-learn PyTorch XGBoost LightGBM etc.)
- Demonstrated experience integrating ML systems into complex product architectures ideally including monolithic applications
- Experience defining integration boundaries solving backend/ML interface issues and collaborating with infra teams on serving patterns
- Strong understanding of experimentation design causal analysis and the relationship between offline and online evaluation
- Excellent communication skills for conveying technical constraints and integration trade-offs
- A strong ownership mindset centered on reliability maintainability and long-term system health
Bonus points if you have:
- Background in causal ML or uplift modeling
- Experience with paywall optimization monetization systems or churn modeling
- Knowledge of real-time inference architectures feature stores or streaming systems
- Publications or open-source contributions in ML RL causal inference or system integration
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 $190000 - $274500 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 peoples 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 were excited about passionate people joining our teameven if you dont 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.
Quizlets 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
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Required Experience:
Staff IC
Key Skills
- Computer Science
- Docker
- Kubernetes
- Python
- VMware
- C/C++
- Go
- System Architecture
- gRPC
- OS Kernels
- Perl
- Distributed Systems
About Company
Quizlet makes simple learning tools that let you study anything. Start learning today with flashcards, games and learning tools — all for free.
