Member of Technical Staff (ML Engineer, Recommendations & User Modeling)
Perplexity · San Francisco
Perplexity is seeking experienced ML engineers to design, build, and optimize the recommendation systems that power core experiences on Perplexity. Perplexity builds AI for those who expect more. Our products are designed to help people find answers, make their most consequential decisions, and complete increasingly ambitious work. Across these use cases, the Perplexity experience must feel deeply personal. Great recommendations and user understanding are central to delighting and delivering for each user. 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. WHY PERPLEXITY IS DIFFERENT - Craftsmanship. We build high quality, tasteful products targeting both AI native and AI curious users. - Ownership. You identify the problem, design the solution and ship it. - Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users. - Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. - Partnership. We amplify each others’ strengths, break down silos, and give selflessly to help our colleagues deliver excellence. 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 eva