Semantic & ML Engineer, AI Knowledge Foundation

vor 2 Wochen

Zürich, Zürich, Schweiz e2c7132a-70ad-45b2-a4b5-8d0cbfaabf3c Vollzeit CHF 80'000 - CHF 120'000 pro Jahr

The Problem:
Generative AI often "hallucinates" or gives generic answers because it lacks deep, structured understanding of specific enterprise knowledge. On the flip side, traditional symbolic AI is precise but struggles with the vast, messy, and implicit relationships in real-world data. We need to bridge this gap to build truly intelligent and trustworthy AI solutions.

Your Mission:
As a Semantic & ML Engineer, your mission is to forge that bridge. You will be at the heart of how we extract, represent, and reason over enterprise knowledge. You'll combine the precision of semantic graphs with the power of machine learning, creating the "understanding layer" that enables our platform to power trusted AI agents. Your work will directly impact how our customers unlock insights and build accurate, context-aware AI.

Here's what you'll actually be doing:

  • Build the Brain's Schema:
    You'll be defining and expanding our RDF/RDFS/OWL ontologies, making sure our knowledge graphs can truly represent complex enterprise information. This isn't just modeling; it's designing the very language our AI uses to understand the world.

  • Teach the AI to Read the Graph:
    You'll design and implement our embedding pipelines (think RDF2Vec, Node2Vec, Word2Vec) that translate the rich structure of our knowledge graph into vector representations. This allows ML models to "see" and understand relationships that are otherwise hidden.

  • Infer and Discover:
    You'll integrate and leverage reasoning engines (Pellet, HermiT) to automatically infer new facts and relationships, significantly enriching our knowledge graph. You'll also use SHACL for validation, ensuring our data quality is rock-solid.

  • Connect Vectors to Queries:
    You will integrate these powerful embeddings directly into our SPARQL extensions, enabling hybrid reasoning where symbolic queries can leverage vector similarity for more intelligent and flexible information retrieval.

  • Measure and Improve:
    You'll constantly evaluate the quality of our embeddings and reasoning outcomes, driving incremental training and improvements to ensure our platform is always learning and getting smarter.

The Tools You'll Wield (Our Tech Stack):

  • Semantic Core:
    You live and breathe RDF, RDFS, OWL, and SPARQL (SELECT, CONSTRUCT, UPDATE). You know how to make a knowledge graph sing.
  • ML Workbench:
    Python 3.9+ is your primary language, with heavy use of NumPy, scikit-learn, and gensim for numerical computing and machine learning.
  • Embedding Wizardry:
    You'll be working hands-on with random-walk generation, HDF5 for model storage, and HNSW for lightning-fast vector similarity search.
  • Reasoning Power:
    Experience with reasoning engines like Pellet or HermiT and SHACL validation is key.
  • Bonus Points:
    If you've played with Graph Neural Networks, advanced transformer embeddings (BERT/Sentence-Transformers), or structured vocabularies like , you'll hit the ground running.

This role is for you if:

  • You're excited by the challenge of making AI truly understand and reason over complex, structured knowledge.
  • You see the immense potential in combining symbolic AI's precision with machine learning's flexibility and you're excited to become an expert in the emerging field of Neuro-Symbolic AI.
  • You're a pragmatic builder who can take complex theoretical concepts and turn them into robust, production-ready systems.
  • You want to build something foundational that will enable the next generation of trustworthy and intelligent enterprise AI applications.

Ready to build the future? Let's talk.

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