Machine Learning Engineer, Personalization and Recommendations

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 models, deep ranking networks, and ANN-based vector search for real-time personalization.
• Build and maintain model training, evaluation, and deployment pipelines, ensuring reliability, training-serving consistency, observability, and robust monitoring.
• Partner with Product and Data Science to translate learner objectives such as engagement, retention, and mastery into measurable modeling goals and experiment designs.
• Advance evaluation methodologies, contributing to offline metric design such as NDCG, CTR, AUC, and 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 in production environments.
• Stay informed on industry and research trends, evaluating opportunities to meaningfully apply them within Quizlet’s ecosystem.
• Mentor engineers, supporting technical growth, experimentation rigor, and responsible ML practices.
• Champion collaboration, inclusion, curiosity, and data-driven problem solving, contributing to a healthy and productive team culture.
• Depending on level, contribute to broader technical strategy, guide architectural decisions, and influence personalization direction across teams and product surfaces.

What you bring to the table:

• Minimum 5+ years of experience in applied machine learning or ML-heavy software engineering, with a strong focus on personalization, ranking, or recommendation systems.
• Demonstrated impact improving key metrics such as CTR, retention, engagement, or other learner-facing outcomes 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, such as Two-Tower models, deep cross networks, Transformers, MMoE, or similar approaches, and the ability to apply them to real-world problems.
• Experience with large-scale embedding models and vector search systems, including FAISS, ScaNN, or similar technologies.
• Proficiency in experiment design and evaluation, connecting offline metrics such as AUC, NDCG, and calibration with online A/B test outcomes to drive product decisions.
• Clear, effective communication, with the ability to collaborate well with product managers, data scientists, engineers, and cross-functional partners.
• A growth and mentorship mindset, helping elevate team quality in modeling, experimentation, and reliability.
• Commitment to responsible and inclusive personalization, ensuring our systems respect learner privacy, fairness, and diverse goals.

Additional strengths for Senior Staff candidates:

• Experience shaping technical strategy across teams or disciplines, balancing long-term architectural vision with near-term product and business priorities.
• Demonstrated leadership through influence, including aligning stakeholders, guiding teams through ambiguity, and driving accountability for outcomes.
• Ability to communicate complex technical trade-offs clearly to senior leadership and cross-functional audiences.
• Experience mentoring senior engineers or applied scientists and helping raise the technical bar across an organization.
• Proven ability to lead large, ambiguous initiatives and influence platform, product, and modeling direction at scale.

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  base salary of $175,000 to $330,000 depending on location, level (Senior, Staff, or Senior Staff), 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