Senior Software Engineer, Pinecone

• This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure
• 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
• 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
• 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
• 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."

Desirable

• We are hiring a senior software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – knowledge 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
• It is a high impact role with significant ownership across architecture, performance, and system reliability
• 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