Job Overview
Location
US Remote
Job Type
Full-time
Category
Product Management
Date Posted
May 6, 2026
Full Job Description
📋 Description
- • As a Staff/Principal Product Manager on the database team at Pinecone, you will drive the strategy and roadmap for core vector database capabilities, focusing on performance, scalability, efficiency, and search quality to enable accurate and performant AI applications at scale.
- • You will spearhead the development of core database features from concept through launch, collaborate cross-functionally with engineering, marketing, sales, and customer success, collect user feedback, define and prioritize requirements based on user personas and market research, and forecast timelines while communicating strategy to leadership.
- • Pinecone is the leading vector database for AI applications, serving over 9,000 customers across industries, backed by $138M from top-tier investors, and headquartered in New York with a remote-first culture.
- • You will deepen your expertise in technical product management within the AI infrastructure space, own end-to-end delivery of high-impact database capabilities, and contribute to making AI more knowledgeable by shaping tools used by developers building next-generation AI systems.
🏖️ Benefits
- • Comprehensive health coverage including Medical, Dental, Vision
- • Free mental health therapy sessions; free One Medical, Tela-doc, and Health Advocate subscriptions
- • Equity award
Skills & Technologies
About Pinecone Systems Inc.
Pinecone provides a managed, cloud-native vector database that lets engineers build and scale machine-learning applications such as semantic search, recommendation systems, and retrieval-augmented generation. The platform automates indexing, sharding, and updates while offering low-latency approximate-nearest-neighbor queries at billions of vectors. It supports hybrid dense-sparse retrieval, metadata filtering, and real-time inserts and deletes. Available on AWS, GCP, and Azure, Pinecone handles infrastructure, security, and scaling so teams can focus on model development rather than operations.
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