🪃 About Memorang

Our mission is to automate how humans learn and acquire skills. The first step in our journey is to build the AI stack for education to transform the credentialing and publishing industries with the best tools to build curricula, assessments, and apps at scale.

After winning #1 (of 1,500) in the Vercel AI Accelerator we bootstrapped to millions in revenue and profitability while delivering over 200MM assessments via our AI platform. We also just closed our first strategic investment to scale faster and deploy our solution to millions of additional learners.

We’re a lean, talented team that rewards agency, curiosity, and shipping real products that directly impact the lives of millions.

"Memorang has literally the best gen AI results I've seen applied to a real world problem."

• Director of Trust and Safety, Google Deepmind

🎯 Role

As our Graph Engineer, the graph isn't a side feature—it's the foundation of our platform and the most critical system we operate. Our Neo4j knowledge graph fuels intelligent search, adaptive learning paths, and the reasoning layers behind our AI agents.

You'll own the graph end to end: modeling, schema design, query optimization, clustering, ingestion pipelines, and performance at scale. This is your chance to define how millions of learners experience knowledge through a graph you architect.

🛠️ Sample projects could include…

• Building an ingestion pipeline that processes 50M+ content nodes and relationships from multiple publisher sources, with validation and rollback capabilities.
• Simplifying graph adoption by creating internal tooling and documentation that empowers product engineers to write efficient graph queries without your direct involvement.
• Designing a new graph schema that models complex learning prerequisites and competency relationships, enabling personalized learning path recommendations.
• Improving search performance by optimizing a critical Cypher query, reducing execution time from 3 seconds to 200ms through clever indexing and query rewriting.
• Driving graph-based personalization by prototyping a recommendation algorithm using Graph Data Science that increases content discovery by 40%.
• Partnering with Platform to implement Neo4j clustering with automated failover and backup strategies, achieving 99.9% uptime for production workloads.

🤝 You might be a fit if you…

• Have 5+ years of engineering experience with at least 3 years using graph databases in production.
• Have expertise in Cypher query design, profiling, and optimization.
• Have a strong background in graph schema design, clustering, and scaling strategies.
• Have a solid software engineering foundation (TypeScript/Node.js preferred, but open to others).
• Have built and maintained ETL/ingestion pipelines for graph databases.
• Can design and evolve graph schemas, indexes, and constraints that power AI-driven features.
• Can architect, deploy, and maintain Neo4j clusters with high availability, backups, and monitoring.
• Collaborate effectively with AI and full-stack engineers to enable graph-powered workflows.