Deep Learning Scientist

Are you looking for a cutting-edge DeepTech startup specialising in advanced AI systems, agent-based architectures, and knowledge-driven intelligence? Our mission is to revolutionise the way data is analysed, connected, and reasoned about in the field of cybersecurity by combining state-of-the-art deep learning, AI agents, and knowledge graph technologies. As a pioneer in this space, we are looking for highly skilled and passionate Deep Learning / AI Scientists to join our team and contribute to our ongoing research and development efforts. As a Deep Learning Scientist with a focus on AI agents and knowledge-based systems, you will play a key role in the development and implementation of intelligent architectures that combine large language models, autonomous agents, and structured knowledge representations. You will work closely with a team of talented researchers, engineers, and cybersecurity domain experts to design, train, and optimise AI systems capable of reasoning over large volumes of unstructured and structured data. This role offers an exciting opportunity to contribute to cutting-edge research, impact real-world applications, and shape the future of AI-driven cybersecurity intelligence. Tasks Research and Development Conduct state-of-the-art research in deep learning, agent-based systems, and knowledge-enhanced AI. Explore novel architectures such as Transformer-based models (e.g. BERT, GPT), retrieval-augmented generation (RAG), multi-agent systems, and neuro-symbolic approaches to improve reasoning, planning, and decision-making capabilities. Agent and System Design Design and implement AI agents that collaborate, plan, and reason over complex problem spaces. Develop architectures that integrate LLMs with tools, memory, feedback loops, and structured knowledge sources. Knowledge Graphs and Representation Learning Develop and apply techniques for building, maintaining, and leveraging knowledge graphs, ontologies, and semantic representations. Combine symbolic knowledge with learned representations to enhance explainability, consistency, and reasoning performance. Data Preprocessing and Feature Engineering Develop advanced techniques for preprocessing and representing both unstructured text and structured data, including tokenisation, embeddings, entity linking, relation extraction, and graph-based representations. Model Training and Evaluation Train and fine-tune deep learning models using techniques such as transfer learning, self-supervised learning, in-context learning, and reinforcement learning for agents. Evaluate system-level performance using appropriate metrics and propose improvements for robustness and scalability. Collaborative Research Work closely with cross-functional teams including data scientists, engineers, and cybersecurity specialists to understand real-world requirements, contribute to project planning, and translate research into production-ready systems. Documentation and Reporting Document research findings, system architectures, and experimental results clearly and concisely. Prepare technical documentation, internal reports, and presentations for both technical and non-technical audiences. Requirements Ph.D. in Computer Science, Electrical Engineering, or a related field with a focus on machine learning, deep learning, AI systems, or NLP. Exceptional candidates with a Master’s degree and strong practical experience will also be considered. Strong theoretical and practical knowledge of deep learning models and architectures, particularly Transformer-based models and modern LLM ecosystems. Experience with AI agents, multi-agent systems, tool-augmented LLMs, or autonomous reasoning systems is highly desirable. Knowledge Graph & Semantic Technologies: Experience with knowledge graphs, ontologies, entity/relation extraction, graph embeddings, or hybrid neuro-symbolic approaches. Strong Mathematical Foundation: Solid understanding of linear algebra, probability theory, statistics, and optimisation methods. Programming Skills: Proficiency in Python and common deep learning frameworks such as PyTorch, TensorFlow, Langchain, vLLM. You enjoy working in a fast-growing company. You dare to question things and challenge existing assumptions. Benefits Competitive salary Excellent working conditions and flexible working hours Flexible mix of home office and office work (Leipzig center) Workation is possible Use of state-of-the-art technologies with creative freedom