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Nextdoor is redesigning its Search experience to help neighbors quickly locate posts, people, places, and answers. You will shape the end-to-end experience—from the entry point and typeahead through results, filters, and page layout—with a focus on relevance and trust.
You will work with data science, ML, and research teams to balance sponsored and organic results, run experiments, and drive user-centric improvements for a large audience.
You’ve designed search or discovery before and can show the results. You think of search as a conversation rather than a list of links: what happens as someone starts typing, how very different kinds of results (posts, people, places, businesses) sit side by side, how a neighbor narrows things down, and what we say when we come up emptyYou’re also comfortable around revenue. You’ve worked beside ads and monetization teams and can design paid placements that feel like help instead of interruption. This is a long-haul effort, and you’ll have a PM, data scientists, ML engineers, and researchers working alongside youClear communication across disciplines, a bias for action, and the patience and drive for a long rebuildSharp consumer instincts and real curiosity about how neighbors look for local informationComfort designing for experimentation: framing hypotheses with PM and data science, interpreting mixed results, and recommending what shipsA portfolio that shows full-range craft: framing the problem, interaction and visual design, prototyping, and real care for the unglamorous states like loading, empty, and errorFamiliarity with HTML, CSS, JavaScript, Swift, Java, or XcodeExperience partnering with ads or monetization teams, and a point of view on when paid content helps people and when it gets in their wayBreadth across areas like growth, marketplaces, ads, or new-product work, and the adaptability to shift as priorities change5+ years of product design experience, with substantial time on search, discovery, or other dense, content-heavy surfaces used by large audiencesEnough fluency in how search works (query understanding, retrieval, ranking) to design alongside ML and LLM features, including the moments when they get things wrongA record of lifting engagement and revenue through design, with sound judgment about the trade-offs between them