5-7 years of experience in building production recommendation systems at scale
Strong knowledge of ML fundamentals like engagement modeling and online experimentation
Experience with LLM integration in ranking and personalization
Ability to innovate personalization for LLM-native products
Leadership experience in guiding technical direction of recommendation or ranking systems
Responsibilities
Own personalization and ranking for key product features to enhance user utility
Develop user models that identify intent and preferences for personalized experiences
Design decision-making frameworks that balance user satisfaction and business goals
Establish data foundations for systems to learn from user interactions
Contribute to the technical strategy for ranking and personalization systems
Benefits
Collaborative work environment focused on innovation and growth
Opportunities to shape the future of recommendation technology
Access to cutting-edge LLM developments
Engagement in impactful projects that directly influence user experience
Room for creativity and reimagining traditional approaches in AI applications
Full Job Description
Perplexity is seeking experienced ML engineers to design, build, and optimize the recommendation systems that power core experiences on Perplexity.
To do this, we are reimagining recommendation systems for the LLM era. Our goal is to combine the intelligence of frontier LLMs, the personalization context that comes from real product usage, and the continual learning capabilities of modern recommendation systems. We build systems that draw on past context and connected data sources to deeply understand each user's needs and recommend the actions that help them get the most out of Perplexity.
What you'll do
Own the personalization and ranking behind key product surfaces to make Perplexity more useful and drive impact on core user and business metrics.
Build user modeling that captures intent, preference, and propensity, and powers more relevant, more personalized experiences.
Design the decision layer that balances competing objectives to produce the best overall experience for the user.
Build the data and evaluation foundations that let these systems learn and improve with usage.
Help shape the technical direction of ranking, recommendations, and personalization at Perplexity.
What we're looking for
Deep, hands-on experience building production recommendation, ranking, or personalization systems at scale.
Strong ML fundamentals, covering areas such as engagement modeling, model calibration, offline and online metrics, and online experimentation.
Experience integrating LLMs into ranking, retrieval, or personalization pipelines.
Taste and judgment for how personalization should work in an LLM-native product, and curiosity about reimagining it from first principles.
For tech leadership roles, we will also look for prior experience setting technical direction for recommendation/ranking projects.
Nice to have
Experience with large-scale ranking and training infrastructure (multi-stage retrieval and ranking, feature stores, real-time serving).
Background in user understanding, feed ranking, notifications, growth, or lifecycle modeling.
