Ai Safety Researcher

AI Safety Researcher — Early Career

Icaro Foundation · Rome preferred · Flexible arrangements

La descrizione completa del lavoro illustra tutte le competenze, l'esperienza pregressa e le qualifiche che i candidati dovrebbero possedere.

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 who can demonstrate strong research or engineering ability.

A completed PhD, previous AI safety employment, and published papers are not required. We care about the quality of your work, your contribution to it, and your ability to learn.

If you are currently studying, please tell us about your availability and how you would combine