Materials Data Engineer (AI for Science, Knowledge Graphs) - Developer

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 isn’t 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.

• 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.

• 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 won’t 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 let’s shape the future together. datin GmbH Scientific discovery is the engine of human progress, yet the tools researchers use today are stuck in the past: paper notebooks, scattered PDFs, and rigid spreadsheets. datin is here to change that. Based in Karlsruhe, we are building the world’s first AI-native infrastructure for Research & Development. We replace complex data silos with an intuitive Mind-Map interface that mirrors how scientists actually think. Behind the scenes, our platform weaves these workflows into a powerful Knowledge Graph, creating a living “corporate memory.” This allows R&D teams to move from messy trial-and-error to true AI-assisted development, ensuring that no experimental data is ever lost or wasted. We are a startup, founded by experienced scientists. We don’t just plug into the wall; we are building the new power grid for innovation. From materials science to green energy, our software enables the breakthroughs that shape our future.