Senior/staff Software Engineer, Search & Retrieval Infrastructure

Pinecone Pinecone · Data AI · New York, NY · R&D

Senior/Staff Software Engineer to design and build core components of a next-generation knowledge retrieval system for AI, focusing on search and retrieval infrastructure for scalable, enterprise-grade agentic systems. This involves building frameworks for connecting knowledge to LLM-powered applications, leveraging a vector database for semantic and hybrid retrieval. The role emphasizes backend system architecture, distributed systems, and applied AI infrastructure, with significant ownership across architecture, performance, and reliability.

What you'd actually do

  1. Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation
  2. Design and build optimized indexing pipelines for structured and unstructured data
  3. Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration
  4. Improve retrieval quality through evaluation and observability frameworks
  5. Design APIs for internal and external user and agentic consumers

Skills

Required

  • backend system architecture
  • distributed systems
  • applied AI infrastructure
  • high throughput
  • low latency
  • long-term maintainability
  • high-throughput indexing pipelines
  • unstructured data
  • structured schemas
  • semantic search
  • vector databases
  • hybrid retrieval strategies
  • traditional search engines (Elastic, OpenSearch)
  • Retrieval-Augmented Generation (RAG) patterns
  • embedding pipelines
  • query planning
  • metadata filtering
  • Go, Rust, C++, Java, or Python
  • Kubernetes
  • cloud-native architectures
  • observability frameworks
  • Terraform or Pulumi
  • product thinking
  • design clean, intuitive APIs

Nice to have

  • multi-tenant SaaS platforms
  • retrieval evaluation frameworks
  • query planning
  • agentic reasoning loops

What the JD emphasized

  • shipping production-grade backends for large-scale systems
  • high-throughput indexing pipelines
  • semantic search
  • vector databases
  • hybrid retrieval strategies
  • Retrieval-Augmented Generation (RAG) patterns
  • embedding pipelines
  • hybrid search techniques
  • query planning
  • metadata filtering

Other signals

  • vector database
  • semantic search
  • hybrid retrieval
  • LLM-powered applications
  • agentic systems
  • retrieval orchestration