About the role
Hey there! We're 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 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.
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 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.
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