Member of Technical Staff (Software Engineer, Applied AI)

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.

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  Apply state-of-the-art ML and LLM techniques to solve problems spanning:

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    Personalization (LLM memory, context summarization, retrieval and ranking);

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    Contextual recommendations and Monetization applications

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    Build frontier agent capabilities on top of Perplexity Computer

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  Build auto research harness for both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact.

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  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.

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  Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements.

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  Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle.

• 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).

• 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).