Senior AI/ML Engineer, Security Log Intelligence

Tasks

• Develop Security-Log Parsing Methods: Design and implement methods for extracting typed events from heterogeneous SIEM, EDR, NDR, operating-system, and network telemetry.


• Design Embeddings and Retrieval: Select, evaluate, and tune representations and retrieval methods for security events.


• Handle Ambiguity Explicitly: Implement confidence scoring, calibration, and controlled treatment of ambiguous evidence.


• Ground Results in Evidence: Ensure that results are supported by traceable evidence from the original telemetry.


• Build Rigorous Evaluations: Define datasets, baselines, ablations, and metrics, and analyse failure modes systematically.


• Optimise Inference: Make the pipeline practical for cloud operation and on-premises deployment.


• Productise the Research: Work with backend, integration, and offensive-security engineers to turn experimental methods into maintainable services.


• Document the Work: Produce clear experiment records, architecture decisions, and technical reports.


• Contribute to Academic Research: Contribute, at minimum as a co-author, to an academic research paper published in the context of the project.

This is a fixed-term position running until 31 October 2027.

Requirements

You do not need to meet every requirement to apply. We care more about demonstrated depth, sound experimental judgement, and the ability to ship reliable systems than about a specific academic title.

• Machine Learning and LLM Systems: Strong Python programming skills Practical experience with PyTorch or a comparable framework Experience with open-weight language models, structured outputs, embeddings, or retrieval systems Experience with fine-tuning, PEFT, quantisation, model serving, or inference optimisation Understanding of hallucination, calibration, distribution shift, and model failure analysis


• Information Retrieval and Evaluation: Semantic retrieval, ranking, classification, or information extraction Approximate nearest-neighbour search and vector indices Evaluation using metrics such as , MRR, F1, exact match, calibration, and ablation studies Dataset construction, partitioning, and reproducible benchmarking


• Software Engineering: Ability to turn experimental code into maintainable production components Testing, profiling, observability, and performance analysis Experience working with APIs, distributed services, and containerised environments


• Cybersecurity Knowledge: Security logs, SIEM, EDR, NDR, detection engineering, incident response, or threat hunting are strong advantages Understanding of endpoint, process, identity, and network telemetry is a plus


• Research Background: MSc, PhD, or equivalent practical research experience in computer science, machine learning, data science, mathematics, or a related field Ability to read, reproduce, and critically evaluate current research

Mots-clés : Data Processing, Data Engineer.

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