Perplexity is looking for an Applied AI Engineer to design, build, and iterate on cutting-edge agents powering our core experience in Perplexity Computer. Working in this mission critical team, you will develop frontier context layer applications - fulfilling the curiosity of millions of users across the globe. Key Responsibilities - Apply state-of-the-art ML and LLM techniques to solve problems spanning: - Personalization (LLM memory, context summarization, retrieval and ranking); - Contextual recommendations and Monetization applications - Build frontier agent capabilities on top of Perplexity Computer - Build auto research harness for both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact. - Own the entire model lifecycle from research to production: data analysis, modeling, evaluation, offline/online A/B testing, and iterative improvement and build autonomous harness for agent squad to explore different problem spaces. - Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements. - Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle. Preferred Qualifications - 5+ years experience building and shipping robust AI products for large-scale, user-facing or data-driven products. - Strong software engineering skills (Python, production-quality codebases, collaborative development) and experience using agentic coding tools for large scale parallel developments. - In-depth experience with the full AI lifecycle: data analysis, rigorous evaluation, and ongoing monitoring/improvement. - Proven collaborator and communicator; excels in high-velocity, cross-functional teams. - Curious, driven by end-user/product impact, and passionate about advancing the state of applied ML and AI. - BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience). Bonus Points For - Experience with LLM context engineering or harness engineering. - Experience in mid-training or post-training frontier open source models - Experience in large scale user-centric and content-centric personalization challenges (user modeling, retrieval, content ranking, etc).