Deep Learning Scientist (mfd)

Stellenbeschreibung:

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 and 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.