About Pinecone
Pinecone is the knowledge infrastructure for AI at scale. Its leading vector database and knowledge engine, Pinecone Nexus, power accurate, performant AI applications for more than 9,000 customers and 800,000 developers worldwide. Pinecone's mission is to make AI knowledgeable. Pinecone is based in New York and raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital.
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).
Skills Required
- 6+ years of experience in software engineering and architectural design for large-scale systems
- Expertise in at least one programming language such as Go, Rust, C++, Java, or Python
- Experience with semantic search, vector databases, and hybrid retrieval strategies
- Familiarity with Kubernetes and infrastructure-as-code tools like Terraform or Pulumi
- Experience building high-throughput indexing pipelines
What We Do
Pinecone stands as a leader in the AI industry, dedicated to transforming artificial intelligence and tackling its most pressing challenges head-on. Our mission revolves around augmenting the capabilities of AI models, addressing the issue of AI hallucinations, and ensuring the delivery of accurate and dependable results. Pinecone specializes in vector database technology, a groundbreaking innovation that has reshaped data management for AI applications. Our vector database solutions are designed to help eliminate AI hallucinations, making AI outputs reliable and trustworthy. Moreover, Pinecone's technology streamlines data retrieval, minimizes memory overhead, and optimizes AI operations, crucial for the growing demand for Generative AI (GenAI). With strategic partnerships with industry giants like OpenAI, Google Cloud Platform, AWS, and others, Pinecone plays a central role in advancing the AI landscape. Beyond technology, we foster knowledge sharing through events and educational initiatives, empowering AI engineers and leaders with actionable insights. Pinecone is not just transforming AI; we are helping pave the way for a more dependable and scalable AI future.
Why Work With Us
At Pinecone, our culture emphasizes camaraderie, collaboration, and inclusivity. Deeply rooted in our DNA, we prioritize these values. In the dynamic ML/AI realm, we strive for top engineering solutions, while ensuring a shift to AI that aligns with genuine human values.
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