Graph Database Engineer

The Role

We are looking for a senior graph database engineer who does not just know Cypher; you dream in traversals. You are obsessed with ontology. You will own the design, optimization, and scaling of the graph data layer, working directly with the founding team to build the core intelligence engine that powers everything.

This is not a role where you maintain an existing system. You will be architecting from first principles: designing schemas for relationship intelligence, building Graph RAG pipelines that feed the AI copilot, and solving hard problems around temporal relationship tracking, weighted path algorithms, and multi-tenant graph isolation.

Why This Role Exists

We are building a shortlist of exceptional graph engineers for a US-headquartered, Seed startup investing in its intelligence layer. Conversations begin in April, with hiring decisions through June. This is a selective process; we are identifying engineers who should be part of the founding technical team.

What You Will Do

1. Design and evolve the graph database schema for modeling complex professional relationships: people, companies, interactions, affiliations, funding networks, and more
2. Design and implement a Text-to-Cypher layer that translates natural language into efficient and accurate queries, enabling intuitive graph search without requiring query expertise
3. Architect for scale by designing structures that stay performant as the graph grows
4. Define and own graph quality standards detecting duplicate nodes, resolving entity conflicts, and ensuring relationship integrity across high-velocity, multi-source data ingestion
5. Build and optimize Cypher queries and graph traversal algorithms, including shortest path, community detection, and influence scoring
6. Architect Graph RAG systems that combine graph traversal with vector similarity search to power the AI copilot
7. Implement temporal relationship tracking: modeling how connections evolve, strengthen, and decay over time
8. Design multi-tenancy patterns that keep user graphs isolated while enabling shared enrichment layers
9. Build performant data ingestion pipelines that transform unstructured relationship data (LinkedIn, email, calendar) into rich graph structures
10. Collaborate with the founding team on product decisions; your understanding of what graphs can do will directly shape what gets built
11. Establish best practices for graph database operations: monitoring, backup, migration, and performance tuning

What We Are Looking For

Required:

1. 6+ years of software engineering experience, with at least 3 years of deep, production-level work with graph databases (Neo4j, FalkorDB, Amazon Neptune, TigerGraph, or similar)
2. Fluency in Cypher and/or Gremlin: you can write complex multi-hop traversals, optimize query plans, and reason about graph performance at scale
3. Strong data modeling instincts, particularly for relationship-heavy domains (social networks, knowledge graphs, fraud detection, supply chains, or similar)
4. Experience with graph algorithms: centrality measures, community detection, pathfinding, link prediction
5. Comfort working across the stack: you will interface with backend orchestration layers, vector search (Qdrant), and LLM integrations (OpenRouter)
6. Startup DNA: you are self-directed, move fast, communicate clearly, and thrive with ambiguity. Team player and passionate about cutting-edge technology

Valuable:

1. Experience with FalkorDB or RedisGraph specifically
2. Background in Graph RAG, knowledge graphs, or combining graph traversal with LLM-based reasoning
3. Familiarity with vector databases and hybrid search architectures
4. Experience building relationship or network intelligence products
5. Contributions to open-source graph database projects or tooling
6. Understanding of NLP pipelines for entity extraction and relationship identification

Who Thrives Here

You are not humble about your graph expertise. You post in forums. You contribute to open-source. You have given talks at NODES or KGC, or you are the type who should. You think in nodes and edges, and you get excited about solving problems others consider too complex. You want to build the intelligence layer of a product, not just query an existing one.

How to Apply

The application process is intentionally rigorous to ensure a great fit for both sides:

Step 1: Submit Your Application

Complete the application form and upload your resume. Highlight your technical impact and measurable achievements.

Step 2: AI Interview + Code Exercise

Qualified applicants will join a combined AI interview and coding session to assess technical reasoning, problem-solving, and communication.

Step 3: Evaluation and Review

Submissions are reviewed for code quality, clarity, and architectural decisions.

Step 4: Live Interview

Finalists will meet with VamosWatu and the partner's engineering leadership to discuss technical alignment, collaboration style, and culture fit.

Final Step: Selection

Top performers will move forward for final selection and onboarding into the engineering team.