AI Engineer: Applied NLP & Knowledge Graphs
Quick Summary
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,
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
~1 min readYou'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outdated content, and keep the knowledge their teams rely on accurate and trustworthy.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.
React/React Native frontends, Python/Node.js/Java backends, running on GCP (App Engine,
Cloud Run, Kubernetes, Cloud SQL, Firestore, VertexAI).
Responsibilities
~1 min readYou'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.
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
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:
Nice to Have
~1 min readDocker/Kubernetes
Java backend experience
Data engineering and ETL pipelines
DevOps/CICD experience (MLOps/LLMOps)
This role has a clear early bar, and it's an ambitious one. By day 90, success looks like:
A knowledge graph stood up and live in production: the foundation Compass runs its detection on, not a prototype
Working detection: Compass can flag where knowledge is duplicated, stale, or self-contradictory, with claims and entities resolved well enough to trust
Measured on precision and recall, not ranking: you're confident you're surfacing real issues, and just as confident you're not raising false ones
The wider AI team has learned something from how you built it: you've brought knowledge in and spread it
The home run is detection people trust: real issues caught, false positives kept low enough that teams act on what Compass tells them. The misfire is a clever graph that flags noise nobody believes. We're hiring for the first one.
This role is based in Helsinki, Finland. The working mode is Hybrid. If needed, we'll support you
with relocation and Visa application.
What We Offer
~1 min readLocation & Eligibility
Listing Details
- First seen
- June 15, 2026
- Last seen
- June 18, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 51%
- Scored at
- June 15, 2026
Signal breakdown
Please let happeo know you found this job on Jobera.
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