About this role
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.
Your core responsibility is to 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.
You will be at ease with figuring out how to approach new research questions and implementing technical solutions for them at scale.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google (https://www.google.com/about/careers/applications/benefits/).
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
Preferred qualifications:
- Master's degree or PhD in Computer Science or a related technical field.
- Experience in Large Language Model (LLM) development, fine-tuning, or safety evaluation methodologies.
- Experience taking research from concept to product.
- Experience in areas such as Safety and Alignment.
- 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.
- Advance research in automated red-teaming and adversarial robustness, leveraging multi-turn/agentic attacks to systematically test for and uncover misuse vulnerabilities.