AI Safety Researcher

AI Safety Researcher
Icaro Foundation
· Rome preferred
· Flexible arrangements

Si assicuri che tutte le informazioni della sua candidatura siano aggiornate e corrette prima di cogliere questa opportunità.

The work
Icaro Foundation is an independent non-profit AI safety lab based in Rome. We study advanced AI systems: what they can do, how they fail, and how those findings can support developers and institutions responsible for their governance.

We see AI safety as one of the defining scientific and societal challenges of our time.  As AI systems become more capable, autonomous, and widely deployed, understanding and reducing their risks is increasingly urgent. We are looking for people who are deeply interested in these questions and motivated to contribute through rigorous research.
You will  help produce new research and develop the lab’s shared codebase and knowledge base , working closely with our researchers across the research process: reviewing literature, refining questions, implementing experiments, analysing results, and contributing to papers and technical reports.
Our research focuses particularly on  agentic, multi-agent, and compositional safety : how risks emerge across extended interactions, tool use, and systems involving multiple AI agents. We also study  testing awareness and evaluation validity , including whether models behave differently when they recognise that they are being evaluated.
Alongside our research, we evaluate frontier models for international model providers as independent third-party evaluators, using public and proprietary benchmarks and red-teaming environments.
Our public work includes:
Boiling the Frog, on multi-turn agentic safety;
Adversarial Humanities Benchmark, on the robustness of safety behaviour under stylistic reformulations;
research on LLM-to-LLM risks, multi-agent collusion, and interaction-level safety.
You can explore our research programme and papers to learn more.

What you would do
Your work will combine three closely connected areas.
Contribute to research
Review relevant literature, compare methods, and identify questions worth investigating.
Help turn research questions into experimental protocols, including baselines, controls, and clear evaluation criteria.
Implement and run experiments with frontier and open-weight models, including agentic and multi-agent environments.
Analyse results and model traces, investigate unexpected behaviour, and assess confounders and alternative explanations.
Contribute to research papers, benchmarks, technical reports, and presentations.
Develop the research codebase
Write and improve Python code for experiments, evaluations, data processing, and analysis.
Extend existing tools and environments, fix bugs, and participate in code review.
Add tests, documentation, and reproducible configurations so other researchers can inspect, rerun, and build on your work.
Build the lab’s knowledge base
Produce concise, source-grounded notes on papers, methods, benchmarks, and research questions.
Document experimental setups, findings, limitations, and negative results.
Organise and connect references, datasets, code, and research notes so the team can find relevant evidence and reuse previous work.
You may bring stronger skills in research or engineering. The role involves both writing code and reasoning carefully about evidence.

Who should apply
We welcome applications from  master’s students, PhD students, recent graduates, and researchers at the beginning of their careers , including those who have recently completed a PhD.
Relevant experience may come from a thesis, academic research, independent experiments, open-source contributions, internships, or previous employment. We also welcome applicants from non-traditional backgrounds…