Research Scientist, AI Safety and Security
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Job Description
Google will be prioritizing applicants who have a current right to work in Singapore, and do not require Google's sponsorship of a visa.
Minimum qualifications:
- PhD degree in Computer Science, a related field, or equivalent practical experience.
- Experience in machine learning, adversarial machine learning or evaluating frontier AI systems, which includes but not limited to supervised learning, unsupervised learning and reinforcement learning, ML interpretability, adversarial robustness, ML safety, generative models, agentic AI, multi-object optimization.
- One of more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
Preferred qualifications:
- Experience with general purpose programming languages (e.g., Python).
- Experience investigating emerging technical threats (e.g., automated scams, deepfake generation, or rogue agent vulnerabilities) and designing robust, proactive defense mechanisms.
- Demonstrated expertise in adversarial machine learning, AI agent security, data poisoning, prompt injection, and model backdoor detection.
- Strong background in applying a security mindset to artificial intelligence, including debugging complex ML failure modes, reverse engineering model behaviors, and red-teaming frontier AI systems.
- First-authored publications in top machine learning, safety/security tracks in machine learning or AI conferences, or HCI conferences.
About the job
As a Research Scientist, you will join a specialized research effort dedicated to proactive threat mitigation, adversarial machine learning, and agentic security. Our team operates at the trustworthy AI, focusing on securing next-generation AI models and intelligent agents against emerging threats.
In this role, you will focus on the machine learning foundations of AI safety, developing innovative techniques, continual learning, and interpretability to prevent safety drift and enhance intrinsic model robustness. You will co-develop advanced evaluation benchmarks, working alongside academic institutions and global engineering teams to provide research that will form the basis of next-generation trustworthy AI capabilities.
Responsibilities
- Drive foundational machine learning research in model robustness, continual learning, interpretability, and multiobjective optimization to advance trustworthy AI.
- Design and develop rigorous evaluation protocols, scenario-based benchmarks, and stress-testing methodologies to assess frontier AI capabilities and multi-agent consensus.
- Curate advanced datasets and conduct fine-tuning or optimization experiments to enhance model resilience against emerging threats and ensure adherence to safety constraints.
- Collaborate extensively with regional engineering hubs, core product teams, and academic partners to transition theoretical proofs-of-concept into robust production solutions.
- Publish groundbreaking research in machine learning venues and actively participate in academic and industry research communities
Find out how well you fit this job.
Find out how well you fit this job.
Find out how well you fit this job.
About us
Google’s mission is to organize the world‘s information and make it universally accessible and useful.
Since our founding in 1998, Google has grown by leaps and bounds. From offering search in a single language we now offer dozens of products and services—including various forms of advertising and web applications for all kinds of tasks—in scores of languages. And starting from two computer science students in a university dorm room, we now have thousands of employees and offices around the world. A lot has changed since the first Google search engine appeared. But some things haven’t changed: our dedication to our users and our belief in the possibilities of the Internet itself.