---
title: 'Senior/Staff Software Engineer, Search & Retrieval Infrastructure at Pinecone'
canonical: 'https://feeny.ai/job/senior-staff-software-engineer-search-retrieval-infrastructure-pinecone-united-b960jp5y27vv'
type: 'job'
last_seen: '2026-09-07'
---

# Senior/Staff Software Engineer, Search & Retrieval Infrastructure at Pinecone

- **Company:** Pinecone
- **Location:** United States
- **Compensation:** $190k–$270k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-08-05
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/pinecone/7ef089cb-a721-4ad8-a6d0-c390e64991d2

## Job description

## 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 http://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

- Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love.
- Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket."

Bonus Points

- Experience building multi-tenant SaaS platforms.
- Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results.
- Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps).

Perks & 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

All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws.

## About Pinecone

## Company Overview

- **One-liner**: Pinecone is the knowledge infrastructure for AI at scale, providing the leading vector database and knowledge engine for building accurate, performant AI applications.
- **Entity Type**: Private (backed by Andreessen Horowitz, ICONIQ, Menlo Ventures, Wing Venture Capital)
- **Headquarters**: New York City, NY, USA (with an additional office in Tel Aviv, Israel)
- **Founded**: 2019
- **Founders**: Edo Liberty

## Core Business

- **Primary industry/industries**: Vector database / AI infrastructure / Knowledge management
- **Target customers**: B2B – developers, engineering teams of all sizes, enterprise customers building AI applications (search, recommendation, agents, RAG)
- **Mission or purpose statement**: "Make AI knowledgeable."

## Products & Services

- **Pinecone Vector Database**: Fully managed, serverless vector database optimized for low-latency similarity search at billion-scale with automatic scaling, built-in freshness, and a cloud-native architecture.
- **Pinecone Nexus**: Knowledge engine that combines cutting-edge models (embedding, planning, reranking) with flexible query mechanisms (vector, keyword, filtering) for end-to-end retrieval.
- **Pinecone Assistant**: A conversational interface built on the vector database for question-answering over custom data.
- **Pinecone Dedicated Read Nodes**: Isolated read replicas for high-throughput workloads with strict SLAs.
- **Pinecone BYOC (Bring Your Own Cloud)**: Deployment option that runs within a customer’s own cloud environment for compliance and security requirements.

## Market Standing

- **Valuation/Market Cap**: Not publicly disclosed.
- **Key Metric**: Serves more than **10,000 customers** and **1 million developers** worldwide (source: company page).
- **Notable Investors/Partners**: Andreessen Horowitz, ICONIQ, Menlo Ventures, Wing Venture Capital.
- **Growth Signals**: Rapidly expanding product suite (Nexus, Assistant, MCP Server), hybrid workforce scaling, continuous hiring in engineering and product roles, and growing adoption among enterprises building AI agents.

## Competitive Advantages

- **Proprietary Rust-based implementations** of state-of-the-art algorithms delivering industry-leading performance and low latency.
- **Cloud-native architecture** that separates storage from compute and reads from writes, enabling hands-free scaling to billions of vectors and thousands of queries per second.
- **Fully managed serverless experience** – no capacity planning or infrastructure tuning needed; built-in freshness for dynamic workloads.
- **Best-in-class developer experience** with intuitive APIs, SDKs, extensive documentation, and deep ecosystem integrations (e.g., MCP servers, LangChain, LlamaIndex).
- **Cost-effective retrieval** via object-storage-based data tiering and multi-tenant resource packing.

## Strategic Focus

- Deepening the "knowledge infrastructure" platform to power the next generation of **AI agents** and intelligent applications.
- Advancing retrieval quality with sparse-dense embeddings, reranking, and multimodal capabilities.
- Scaling enterprise readiness with BYOC, advanced security features, and compliance certifications.
- Expanding developer community and ecosystem integrations to maintain leadership in the vector database category.

## Why Work Here

- **Work model**: Hybrid in-office/remote with hubs in New York City and Tel Aviv; remote-friendly roles available for many engineering positions.
- **Culture & values**: "Be yourself, Be a friend, Be a pro" – emphasis on authenticity, trust, collaboration, and ownership.
- **Team**: Work with world-class scientists and engineers who previously built large-scale ML and distributed systems at AWS, Databricks, Google, Microsoft, and Yahoo.
- **Benefits**: Competitive base salary + equity, flexible PTO, WFH equipment stipend, annual company retreat and team offsites, comprehensive medical/dental/vision, paid parental leave, mental health resources.
- **Growth**: Early-stage enough to have outsized impact but with strong product-market fit and massive adoption; opportunity to shape the future of AI infrastructure.

## Sources

1. [pinecone.io - Company](https://www.pinecone.io/company/)
2. [pinecone.io - Careers](https://www.pinecone.io/careers/)
3. [pinecone.io - Product](https://www.pinecone.io/)
4. [LinkedIn - Pinecone](https://www.linkedin.com/company/pinecone-io)

## Other roles at Pinecone

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- [Senior/Staff Software Engineer, Experience](https://feeny.ai/job/senior-staff-software-engineer-experience-pinecone-new-york-fjq0yjrm3tn3) — New York, NY
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- [Senior/Staff Software Engineer, Experience](https://feeny.ai/job/senior-staff-software-engineer-experience-pinecone-united-states-wtsbxbdphpe1) — United States