Data Infrastructure Engineer at Pear VC-backed AI infrastructure startup

You will architect the core systems enabling AI agents to reason over fragmented enterprise data. Designing distributed storage, real-time query engines, and semantic knowledge graphs, you’ll solve deep technical challenges at the intersection of high-throughput systems and knowledge representation for Fortune 500 deployments as a foundational engineering hire.

• Design and implement distributed storage layers and consistency protocols optimized for graph-structured data at massive scale.
• Architect high-performance query execution engines capable of performing real-time semantic inference across billions of data relationships.
• Build robust ingestion pipelines that discover and normalize domain knowledge across dozens of heterogeneous, fragmented enterprise data sources.

• Deep systems-level proficiency in Java, C++, or Rust with a focus on multithreading, distributed consistency models, and consensus protocols.
• Proven track record building core infrastructure components like query engines, database internals, or large-scale data partitioning systems.
• Strong architectural intuition for high-QPS environments and the ability to navigate complex trade-offs between availability, latency, and fault tolerance.

• Work directly with founders who built Visa's payment infrastructure and LinkedIn's 900M-member Knowledge Graph to define the next layer of the agentic AI stack.
• Tackle "engine-level" problems rather than just integrating APIs, including building custom distributed storage and semantic inference layers from scratch.
• Secure a high-impact, seed-stage role with a competitive $180k-$240k base and meaningful early equity in a company already deploying with major enterprises.