Job Opportunities API

About Happeo Happeo is a Series B startup revolutionizing how organizations collaborate and communicate through our unified social intranet platform. We combine collaboration tools, knowledge sharing, and internal communications into one seamless solution that helps teams connect and stay aligned. We pride ourselves on our dynamic, collaborative culture that emphasizes delivering high-quality solutions while fostering professional development. No bureaucracy, just smart people building things that matter. About the Role You'll be working with our development team to build Happeo's proprietary technology for intranet information management. The platform helps organizations find gaps, duplication, and outdated content, and keep the knowledge their teams rely on accurate and trustworthy. We're launching into Open Beta, and the next frontier is knowledge that people, and the AI systems they use, can actually trust. This role builds toward Compass, Happeo's new knowledge verification layer. As AI systems like Claude, Gemini, and ChatGPT increasingly answer from an organization's own knowledge, Compass checks whether that knowledge actually holds up, surfacing where it's duplicated, stale, or self-contradictory, and proposing fixes. The job isn't search, it's detection: you'll build the knowledge graph and graph-RAG that understand what the knowledge says and whether it's correct, not just which documents mention what. Our Stack React/React Native frontends, Python/ Node.js/Java backends, running on GCP (App Engine, Cloud Run, Kubernetes, Cloud SQL, Firestore, VertexAI). What You'll Do You'll stand up the knowledge graph and graph-RAG behind Compass from scratch; this doesn't exist here yet, and building it is the job. It's novel work: extracting and structuring an organization's knowledge so issues in it can be detected. You'll ship from zero to production with minimal oversight and real autonomy to define the technical approach, make the architectural calls, and drive direction. This isn't a detailed-specs role: you'll form opinions about what to build, how, and why it matters, bring in knowledge the team doesn't have yet, and actively spread it. Your Typical Day Build information extraction pipelines that turn messy documents into structured facts: entities and the relationships between them Build claim extraction and entity resolution: pull atomic, verifiable claims from documents, and decide when two extracted things are the same entity Detect where knowledge is duplicated, stale, or self-contradictory, and prove it works without crying wolf Stand up the knowledge graph the detection runs on, and the graph-RAG layer that supports it alongside conventional RAG Own the impact end to end: ship, measure, iterate, fail fast, learn faster Evangelize what you bring in: level up the wider AI team so the knowledge sticks past you What We're Looking For The core of this role is applied NLP: turning messy, unstructured knowledge into verifiable structure. That's where most of the work, and most of the difficulty, lives: Information extraction from unstructured text: entity and relationship extraction, plus ontology and taxonomy development, turning messy documents into structured facts Claim extraction and fact-checking: pulling atomic, verifiable claims out of documents (e.g. "SLA = 72h") and reasoning about whether they conflict (natural-language inference), are stale, or are unverified. This is the hard part of "self-contradictory" and a graph-RAG-for-search background often won't have touched it. Entity resolution and deduplication: deciding two extracted things are the same entity and canonicalizing them; spotting when two documents are versions of each other Strong, practical experience applying NLP and LLMs to real problems Knowledge graphs and graph databases, required as the foundation the detection runs on: Neo4j or similar, fluen