About this role
UE06: £34,610 - £39,906 per annum
College of Science and Engineering / School of Informatics
Full-time: 35 hours per week
Fixed-term: 6 months
The University of Edinburgh is one of the world’s leading research universities, and the School of Informatics is one of the largest and most highly regarded centres for computer science and artificial intelligence research in Europe.
The Opportunity:
The University of Edinburgh has been awarded a generous donation from Amazon to conduct a study on the trustworthiness of AI/ML tools used in hiring and recruitment. The research will investigate the fairness and adversarial robustness of machine learning systems deployed in the recruitment space, such as automated resume screening, candidate assessment, and the detection of fraudulent, machine-generated applications.
We are seeking a motivated Research Assistant (RA) with expertise in cyber security and privacy in applied research. Strong software engineering skills and good knowledge of ML/AI software development stacks are essential. The RA’s work will mostly consist of implementing experiments designed in collaboration with, and under the direct supervision of Dr Marc Juarez and a PhD student in the School.
Your responsibilities will include:
Implementing, running, and documenting experiments that evaluate the fairness and adversarial robustness of AI/ML hiring tools, including LLM-based systems. Developing and maintaining reproducible research software, experimental pipelines, and evaluation benchmarks. Collecting and responsibly managing datasets used in the experiments (e.g., resume and career-trajectory data, including synthetic data). Contributing to the analysis and write-up of results for publication at leading venues in security, privacy, and machine learning.
Your skills and attributes for success:
A degree (BSc/MSc) in computer science, artificial intelligence, cyber security, or a related discipline, or equivalent practical experience. Strong software engineering skills, particularly in Python, and good knowledge of ML/AI development stacks (e.g., PyTorch, Hugging Face). Familiarity with applied research in cyber security and privacy. Ability to design, implement, and clearly document rigorous, reproducible experiments. Desirable: experience with large language models, adversarial machine learning, or algorithmic fairness; experience handling sensitive data responsibly.
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