Overview
About zaimler
Full job description
About zaimler
zaimler is building the next-generation semantic platform that links fragmented enterprise data and extracts meaning with knowledge-distilled models. We’re creating the foundation for AI systems that don’t just generate, but retrieve, link, and reason over enterprise knowledge.
In just over a year, we’ve begun partnering with Fortune 500 design partners in insurance, travel, and technology, deploying our semantic platform into some of the world’s most complex and high-volume data ecosystems. Our platform enables enterprises to make their data AI-ready from the start: automating ontology creation, data mapping, and retrieval-augmented reasoning at scale.
Our team comes from LinkedIn, Visa, Meta, and Branch, and has spent decades solving data and infrastructure challenges at scale. Backed by top VCs, we’re building the next foundational layer for enterprise AI.
What You’ll Do
zaimler's query engine powers graph-native queries across a two-layer system: an internal storage and indexing layer, and a pass-through layer that queries live into customer data platforms like Snowflake and Databricks. You'll work across the engine's coordinator, compiler, and executor, extending language and function support, and helping build queries that reason across multiple data sources at once.
Extend the query engine's language and function support over time Help build cross-source query capabilities: answering questions that span multiple data sources in a single query Work on the storage and indexing layer that the query engine runs against Build and maintain the layer that queries live into external systems like Snowflake and Databricks Work directly in the engine's core components: coordinator, compiler, executor
Strong database fundamentals: you understand consistency models, transactions, and how storage layers actually work under the hood, not just how to query one Experience running systems at real production scale, where naive approaches start to break down A systems mindset: background in OS, file systems, distributed systems, or database internals, from industry or research Ability to work from ambiguous, evolving problems rather than fully scoped tickets A bias toward hands-on execution over pure architecture or process ownership
Experience with graph query languages (e.g. Cypher) API development experience Background at a data intensive company or research lab Prior experience at an early-stage startup, developing systems and processes from scratch.
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