Research Scientist, AI Safety and Security

Google Inc.; Singapore; On-site; SGD 180,000 - 280,000; Full time

Job summary

Google Inc. in Singapore is seeking a Research Scientist to advance threat mitigation for frontier AI and adversarial systems.

The role focuses on ML foundations for AI safety, robustness, interpretability, and continual learning, working with academic partners and global teams to shape next-generation trustworthy AI capabilities. You will develop new evaluation benchmarks, contribute to responsible AI research, and help translate theoretical ideas into robust production solutions across

Qualifications

• PhD in CS, related field, or equivalent practical experience.
• Experience in ML, adversarial ML or evaluating frontier AI systems (supervised/unsupervised/RL, interpretability, robustness, safety, generative models, agentic AI, multiobjective optimization).
• One or more scientific publication submissions to conferences, journals, or public repositories (e.g., CVPR, NeurIPS, ICML, ICLR).

Responsibilities

• Drive foundational ML research in model robustness, continual learning, and interpretability, and multiobjective optimization to advance trustworthy AI.
• Design and develop rigorous evaluation protocols, scenario-based benchmarks, and stress-testing methodologies for 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 safety constraints.
• Collaborate with regional engineering hubs, core product teams, and academic partners to transition proofs-of-concept into robust production solutions.
• Publish groundbreaking research in machine learning venues and engage with academic and industry research communities

Skills

Adversarial ML

Publications

ML research

Education

PhD in Computer Science

Tools

Python

Job description

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, and 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