Our team focuses on de-risking model launches by defending against critical misuse domains such as Cybersecurity, CBRNE (Chemical, Biological, Radiological, Nuclear, Conventional Explosive), and Harmful Manipulation. Currently, this involves building novel evaluations, red-teaming, researching and deploying advanced mitigations (both in-model and out-of-model), and monitoring emerging risks. We ensure that our mitigations are highly robust, while still enabling the beneficial use of our technology. GDM is a dedicated scientific community, committed to ‘solving intelligence’ and ensuring our technology is used for widespread public benefit. The Frontier Safety Mitigation team operates in a fast-paced, highly collaborative environment. We have a strong culture of support, dedication, and teamwork. We take the possibility of tangibly dangerous model capabilities seriously as AI advances. Because of this, we believe that proactively researching and implementing robust, defense-in-depth mitigations is a critical part of the overall strategy for building safe AI. We are looking for a research engineer for the Frontier Safety Mitigation team within the Gemini Safety team. In this role, you will help us build the next generation of safety mitigations for frontier models. This role is highly applied and focuses on building robust, end-to-end defenses against severe risks. This work feeds directly into DeepMind's Frontier Safety Framework commitments.

Build advanced classifiers and data pipelines to detect misuse, owning the end-to-end process from automated evaluation to rapid model iteration.

Build cross-context monitoring systems to detect coordinated harms, developing novel signal aggregation methods across disparate user sessions to identify large-scale attack vectors.

Implement data-driven, semi-automated account-level response systems to detect, track, and apply strikes against persistent malicious actors using rich signals from production traffic.

Evaluate and secure agentic AI systems by developing threat models, creating testing environments, and deploying robust mitigations against frontier-level agentic hacking and long-horizon attacks.

Be able to advance research in automated red-teaming and adversarial robustness, leveraging multi-turn/agentic attacks to systematically test for and uncover misuse vulnerabilities.