Member of Technical Staff (ML Engineer, Recommendations & User Modeling)
San Francisco • FullTime
Posted 3mo ago
Job Location
San Francisco
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
Perplexity is seeking experienced ML engineers to design, build, and optimize recommendation systems that power core experiences. We are reimagining recommendation systems for the LLM era, combining frontier LLMs, personalization context from product usage, and modern recommendation system capabilities. Our systems draw on past context and connected data sources to deeply understand user needs and recommend actions that help them get the most out of Perplexity. We value craftsmanship, ownership, entrepreneurship, scholarship, and partnership.
Responsibilities
- Own personalization and ranking for key product surfaces to enhance usefulness and drive core user and business metrics.
- Build user modeling to capture intent, preference, and propensity for more relevant, personalized experiences.
- Design the decision layer to balance competing objectives for optimal user experience.
- Develop data and evaluation foundations for system learning and improvement.
- Shape the technical direction of ranking, recommendations, and personalization.
Requirements
- Deep, hands-on experience building production recommendation, ranking, or personalization systems at scale.
- Strong ML fundamentals including engagement modeling, model calibration, offline/online metrics, and online experimentation.
- Experience integrating LLMs into ranking, retrieval, or personalization pipelines.
- Taste and judgment for personalization in LLM-native products, with curiosity for reimagining it from first principles.
- Prior experience setting technical direction for recommendation/ranking projects (for tech leadership roles).