Staff Infrastructure Software Engineer, Search Platform
Role Description
As a Tech Lead Search Infra Engineer on the Search Platform team, you will be instrumental in designing, building, and scaling a next-generation search and retrieval platform from scratch. This platform will power Dropbox Dash, AI-driven search, Retrieval-Augmented Generation (RAG) pipelines, and AI agents, enabling intelligent, real-time information discovery and automation.
This role requires deep expertise in search infrastructure, indexing, retrieval models, ranking optimization, and distributed systems. You will own the end-to-end development of search capabilities, including query execution, hybrid search (lexical, vector, and structured search), and real-time AI retrieval.
What’s the Impact?
The search infrastructure you build will be mission-critical to Dropbox’s AI strategy. It will power intelligent assistants, real-time knowledge retrieval, and automation, driving AI adoption and engagement across the company’s product ecosystem.
Why Join?
- Define the Future of Search & AI Retrieval – You’ll be at the forefront of AI-driven search innovation, architecting a system from scratch that will power AI agents and intelligent assistants.
- Work on Cutting-Edge Infra & ML – Gain deep exposure to search infrastructure, RAG pipelines, and AI-powered knowledge retrieval.
- Massive Scale & Impact – Build a search platform handling trillions of documents, used by millions of users worldwide.
- Collaborative, High-Growth Team – Join a team where ownership, innovation, and impact are at the core of what we do.
- Architect and Build Search from the Ground Up – Design and implement a highly scalable, AI-powered search and retrieval platform to support both traditional search and RAG-powered AI workflows.
- Unify Search & AI Retrieval – Develop a single, cohesive search system that integrates with AI agents, copilots, and automated workflows, enabling intelligent search experiences.
- Optimize Indexing & Query Execution – Scale high-performance indexing pipelines to handle trillions of documents while optimizing for latency, cost-efficiency, and relevance.
- Power AI Agents & RAG Pipelines – Build real-time retrieval infrastructure that enables AI assistants to dynamically fetch, synthesize, and generate insights from structured and unstructured data.
- Enhance ML-Driven Ranking & Relevance – Implement hybrid retrieval models (keyword, semantic, and structured queries), and embeddings-based search for AI interactions.
- Scale Search & Retrieval for AI – Ensure global scalability, and multi-language support, adapting search experiences for diverse user needs.
- 10+ years of professional software development experience as well as 5+ years of experience building and operating large-scale search platforms.
- Expertise in search engine architecture, distributed indexing, retrieval models, and ranking optimization.
- Proficiency in lexical search, vector search, embeddings-based retrieval, and hybrid search techniques.
- Strong experience with Elasticsearch, OpenSearch, or custom search infrastructure.
- Solid programming skills in Python, Go, C++, or Java, with distributed systems experience.
- Familiarity with LLMs, AI-powered retrieval, and RAG pipelines is a plus.
- Capable of articulating, conceptualizing, and defining comprehensive strategies
- Exhibits a very high standard of technical judgement, innovation and execution to tackle open-ended problems that require difficult prioritization.
- Proficiency in defining both objectives and the methodologies for achieving them.
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