Machine Learning Engineer, Monetization & Decision Systems
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Job Location:
San Francisco, CA - USA
Monthly Salary:
$ 174000 - 330000
Posted on:
8 hours ago
Vacancies:
1 Vacancy
Job Summary
About the Team:
We are looking for Machine Learning Engineers ranging from the Senior to Staff levels (note: leveling decisions made through the interview process).
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.
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:
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.
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
- Determine when and how to present paywalls discounts or value exchanges
- Selecting personalized study modes or interventions based on learner state intent and context
- Triggering retention and churn-prevention actions at the appropriate moment
- Balancing short-term conversion and revenue goals with long-term engagement retention and learning outcomes
- Prioritize: Multi-objective optimization across monetization retention user experience and learning outcomes time-aware and eligibility-aware decisioning rather than static prediction consistent action selection across sessions devices and product surfaces and an approach that connects offline modeling metrics to online experimental results
- Apply and advance uplift modeling survival analysis sequential decisioning and other policy-based approaches taking responsibility for bringing these techniques into production-grade systems
- 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 specifications
- Collaborate closely with product managers backend engineers and infrastructure partners to ensure ML systems fit naturally into the existing architecture without introducing brittle dependencies
- 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:
- 6 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 $174000 - $330000 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
Required Experience:
IC
About Company
Quizlet makes simple learning tools that let you study anything. Start learning today with flashcards, games and learning tools — all for free.
