ML Engineer, Personalization & Recommendations

Perplexity; Palo Alto (CA); On-site; USD 220,000 - 405,000; Full time

Job summary

Perplexity is seeking experienced ML engineers to design, build, and optimize the recommendation systems that power core experiences on Perplexity.

We aim to combine frontier LLM intelligence with real usage context to deeply understand each user and deliver highly personalized experiences. You will own personalization and ranking, build data foundations, and shape the technical direction of our ranking systems in the LLM era.

Qualifications

• Deep hands-on experience building production recommendation, ranking, or personalization systems at scale.
• Strong ML fundamentals covering engagement modeling, model calibration, metrics, and experiments.
• Experience integrating LLMs into ranking or personalization pipelines.
• Taste and judgment for how personalization should work in an LLM-native product; curiosity to rethink from first principles.
• For tech leadership roles, prior experience setting technical direction for recommender projects.

Responsibilities

• Own the personalization and ranking behind key product surfaces to improve user and business metrics.
• Build user modeling that captures intent and preference to power personalized experiences.
• Design the decision layer balancing competing objectives for the best user experience.
• Build data and evaluation foundations that let these systems learn and improve with usage.
• Help shape the technical direction of ranking, recommendations, and personalization.

Skills

Recommendation systems

Personalization

Ranking models

ML fundamentals

LLMs integration

Tools

ML infrastructure

Job description

Perplexity is seeking experienced ML engineers to design, build, and optimize the recommendation systems that power core experiences on Perplexity.

We aim to combine frontier LLM intelligence with real usage context to deeply understand each user and deliver highly personalized experiences. You will own personalization and ranking, build data foundations, and shape the technical direction of our ranking systems in the LLM era.