AI for Science (Materials, Chemistry, Knowledge Graphs, Ontologies) Developer (mfd)

Hey there! Were datin GmbH and we are building the new power grid for scientific innovation.

Traditional scientific knowledge is still largely hidden from LLMs because physical R&D data (like lab experiments simulations and equipment logs) is rarely recorded in a structured machine-actionable way. Text alone isnt rich enough to support automated discovery.

To bridge this gap we have built an ontology-driven schema-based knowledge graph management system. Now we are taking it to the next level: building autonomous goal-oriented AI agents that can interact directly with our graph databases augment them with new data and identify emerging patterns in physical science.

This role offers a unique opportunity to design production-grade AI agent systems from scratch collaborating closely with experienced material scientists tribologists and software engineers. At datin we value curiosity impact and trust and we design our agent-driven workflows to empower scientists not replace them.

Tasks

• Agentic Workflows: Design and build end-to-end agentic architectures. You will build tool-calling loops memory layers and execution environments that allow agents to query update and validate our graph databases.
• AI Infrastructure: Engineer deploy and maintain performant agent and LLM serving infrastructures both locally and in the cloud.
• Graph-Grounded LLMs: Fine-tune or optimize open-source LLMs to reliably translate natural language scientific requests into structured queries sent to our SDK and accurately traverse complex ontologies.
• Machine Learning for Science: Train and integrate specialized ML models to solve multi-objective optimization problems (e.g. predicting material properties or chemical reactions) that AI agents can use as tools.
• Semantic Digital Twins: Translate real-world physical workflows into semantically-typed knowledge graphs.

Requirements

• Technical Core: Deep practical experience with Agentic frameworks orchestrators or tool-use libraries.
• Software Engineering: Strong proficiency in Python and/or JavaScript with a focus on writing clean modular and well-tested production code.
• Modeling Skills: Hands-on experience building training or fine-tuning models using machine learning frameworks like PyTorch or similar.
• Validation: Familiarity with SHACL RDF RDFS OWL and SPARQL or similar (like CYPHER) validation languages is a strong plus.
• Background: A degree in Computer Science Information Science or Chemistry Materials Science Mechanical Engineering or a related field.
• Mindset: You are meticulous and logical. You enjoy solving the puzzle of how to structure the world into a database.

Benefits

• Flexible working hours
• Free beverages
• Public transportation benefits
• Remote work possible
• Travel expenses compensation

This is the future of scientific AI. At datin GmbH you wont just be writing code; you will be defining the grammar of scientific discovery. If you are ready to build the engine that powers the next generation of R&D apply now and lets shape the future together.