Join early. Build the graph layer for AI-native data systems.

You will work on the core graph runtime: storage layout, traversal execution, schema mapping, persistence, and the pieces that make an embedded graph server feel boring in production.

This is for someone who has strong Rust instincts, reads database papers without ceremony, and can make sharp tradeoffs between elegant internals and a product users can actually ship.

Good signal

• Built database, streaming, query, compiler, or distributed systems components.
• Strong Rust, including async, memory ownership, profiling, and API design.
• History with graph databases, Gremlin, Cypher, TinkerPop, RDF, or knowledge graph infrastructure.
• Comfort owning correctness, benchmarks, docs, and customer-visible behavior.

You will turn messy customer data into useful graph surfaces. That means schema discovery, graph projection design, connector validation, performance debugging, and repeatable patterns for teams moving from SQL tables to graph APIs.

The right person has lived inside production data platforms and can tell when a graph model is clean, when it is overfit, and when the upstream data is lying.

Good signal

• Deep history with Spark, Flink, dbt, Iceberg, Delta, Hudi, warehouses, or streaming systems.
• Can model entities, events, edges, and derived relationships from real schemas.
• Has built pipelines customers depended on and debugged them under pressure.
• Enough graph experience to reason about traversal cost, fanout, and materialization.

You will own the product shape from embedded developer experience through managed cloud workflows. The job is equal parts customer discovery, technical writing, roadmap judgment, and making hard complexity disappear from the product.

You should be comfortable talking to staff engineers, data platform leads, AI teams, and founders, then turning their pain into crisp product decisions.

Good signal

• Built or led products for databases, data platforms, developer tools, AI infrastructure, or observability.
• Can write clear docs, specs, launch notes, and positioning without hiding behind jargon.
• Strong intuition for APIs, onboarding, pricing surfaces, and enterprise adoption paths.
• Comfort making decisions with incomplete data and changing them when users prove you wrong.

You will help technical buyers understand where CrabGraph fits, prove it on real data, and carry the field signal back into product. This is not slideware. You will build demos, inspect schemas, write traversals, debug connectors, and earn trust with engineers.

The best fit has sold infrastructure without losing technical credibility and can move from whiteboard architecture to a working prototype quickly.

Good signal

• Experience with data infra, databases, search, graph, analytics, ML platforms, or security products.
• Can run discovery with data engineers, architects, and AI product teams.
• Comfort with SQL, basic programming, cloud networking, and benchmark skepticism.
• Can write crisp technical follow-ups that unblock evaluation and implementation.

You will make CrabGraph understandable. That means examples, docs, reference apps, talks, benchmarks, tutorials, and honest technical content for developers trying to build graph-backed AI systems.

This role is for someone who can build real software and write about it with precision. You should care about whether readers can reproduce what you published.

Good signal

• Strong writing portfolio for developer audiences.
• Hands-on experience with data systems, graph, AI apps, backend engineering, or distributed systems.
• Can create examples in Java, Python, TypeScript, or Rust without turning them into toy demos.
• Good taste around docs structure, API learning curves, and launch content.