Building Scalable Academic Search Engines

Academic Research Engineer We are creating an integrated AI research platform that streamlines the entire academic discovery process. Our system understands how papers connect, ranks relevance, summarizes insights, and maps citations. This removes weeks of manual work. We serve researchers globally, from PhD students to academic professionals, helping them go from query to a comprehensive, cited review in minutes. The Role Develop and optimize semantic search pipelines, graph-based retrieval, and agentic systems. Design and deploy scalable APIs and services capable of serving hundreds of concurrent requests. Architect, implement, and productionize vector databases and embedding-based retrieval systems. Work on GenAI integrations including react agents, graph agents, prompt engineering, and LLM orchestration. Set up full-stack search engines from scratch that can handle real-world academic workloads. Contribute directly to the infrastructure that makes cutting-edge science more efficient. This is a hands-on role with real ownership. You will move fast, experiment, and ship production systems that impact thousands of researchers worldwide. Requirements Strong background in semantic search, vector databases, LLM frameworks, agent-based systems, and document retrieval. Skilled in Python for backend development with the ability to build scalable APIs and services. Past experience deploying systems at scale, including search engines, retrieval pipelines, or embedding models. Ability to design infrastructure that serves hundreds of concurrent requests. Experience setting up and hosting APIs with public access while balancing speed, cost, and reliability. A research-driven and pragmatic mindset with a focus on measurable outcomes.

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