Senior/Staff Software Engineer, Search & Retrieval Infrastructure

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Pinecone

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

💰 $28M Series A on 2022-03

Artificial Intelligence • SaaS • Enterprise

Pinecone is a leading knowledge platform that focuses on building accurate, secure, and scalable AI applications using its vector database. The company is known for powering AI solutions for top companies, including Cisco, by enabling precise and secure search through its integration with Google Cloud. Pinecone offers tools for embedding, upserting, and indexing data via an easy-to-use API, supporting various AI workloads like multi-modal search and classification. With a strong commitment to security and compliance, Pinecone is SOC2 Type II certified and HIPAA compliant, making it an enterprise-ready solution, fully managed in major clouds such as AWS, Azure, and GCP.

📋 Description

• 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

🎯 Requirements

• Proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems • Comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. • Direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • Understanding 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. • Expert in at least one major language like Go, Rust, C++, Java, or Python. • 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. • Comfortable in a high-growth environment.

🏖️ Benefits

• Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend