infrastructure
4 hours ago*
Senior/Staff Software Engineer, Search & Retrieval Infrastructure
at Pinecone
📍 Global·🏢 Remote
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Responsibilities
- Responsibilities: - Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation - Design and build optimized indexing pipelines for structured and unstructured data - Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration - Improve retrieval quality through evaluation and observability frameworks - Design APIs for internal and external user
Requirements
- About Pinecone Pinecone is the leading vector database for building accurate and performant AI applications at scale in production.
- Pinecone's mission is to make AI knowledgeable.
- More than 9000 customers across various industries have shipped AI applications faster and more confidently with Pinecone's developer-friendly technology.
- About the Team and Role: We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems.
- You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval.
- This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure.
- AI & Retrieval - Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct
- experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. - RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance.
- Technical - Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. - Infrastructure: Familiarity and
- experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi.