Senior/Staff Software Engineer (Search & Retrieval Infrastructure)

Join Pinecone, a leading company in the AI industry, as a Senior/Staff Software Engineer. In this high-impact role, you will design and build core components of our next-generation knowledge retrieval system. You will have significant ownership across architecture, performance, and system reliability. Your responsibilities will include building scalable platform components, optimizing indexing pipelines, improving retrieval quality, designing APIs, and driving technical direction for reliability and security. This is an exciting opportunity for someone passionate about turning data into knowledge and building innovative AI infrastructure.

• Design and build core components of a next-generation knowledge retrieval system, focusing on search and retrieval infrastructure.
• Develop scalable platform components leveraging advanced retrieval techniques, including query planning, semantic and hybrid search.
• Drive technical direction for reliability and security, optimizing latency, throughput, and cost across large-scale inference and retrieval workloads.
• 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
• Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps)
• 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
• Experience building multi-tenant SaaS platforms
• This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure
• Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python
• 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
• 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
• Experience with retrieval evaluation frameworks—knowing how to actually measure "good" search results
• 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
• 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
• 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
• Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket."