Senior/Staff Software Engineer, Search & Retrieval Infrastructure

About Pinecone

Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform—including its Database, Nexus, and Marketplace products—power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io .

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. It is a high impact role with significant ownership across architecture, performance, and system reliability.

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 and agentic consumers

Optimize latency, throughput and cost across large-scale inference and retrieval workloads

Drive technical direction for reliability and security

What You’ll Bring to the Table:

To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge.

Systems Expertise

Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability.

Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas.

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.

Ownership & Impact