Member of Technical Staff (Software Engineer, Desktop Apps)
Perplexity · San Francisco
About the Role
Perplexity AI is looking for experienced Generalist Software Engineers to join our Perplexity and Comet application teams. Our team is reshaping knowledge work through our high-impact desktop and Comet apps. The ideal candidate should have strong programming skills and multiple years of experience shipping high-quality, production software. Our stack spans several different skillsets, including Rust, Kotlin, Swift, Typescript, Chromium and Perplexity's core AI stack. We have found that our strongest engineers are capable of working across multiple codebases and ramping up quickly, and we expect the same high bar for candidates.
Responsibilities
Work closely with product teams to implement novel mobile experiences.
Work closely with design teams to design fast and intuitive UI.
Utilize Perplexity Computer and other Agentic coding tools to accelerate development.
Collaborate with data science and machine learning teams to instrument, analyze, and improve the end-to-end experience.
Work with infrastructure and QA teams on deployment processes, including testing, release, and monitoring.
Requirements
4+ years industry experience
Experience in shipping high-quality native applications
Architecture experience in writing code understandable by both agents and humans
Deep understanding of app performance
Ability to own projects and surfaces, and to push them forward proactively
In information theory, perplexity is a measure of uncertainty for a discrete probability distribution. The perplexity of a fair coin toss is 2, and that of a fair die roll is 6; and generally, for a probability distribution with exactly N outcomes each having a probability of exactly 1 / N, the perplexity is simply N. But perplexity can also be applied to unfair dice, and to other non-uniform probability distributions. It can be defined as the exponentiation of the information entropy. The larger the perplexity, the less likely it is that an observer can guess the value which will be drawn from the distribution.
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