Senior Research Scientist, AI Safety and Security

Google LLC; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Google is seeking a Senior Research Scientist in Singapore to lead a proactive threat mitigation program focused on adversarial machine learning and agentic security. The role emphasizes developing ML safety foundations, continual learning, and interpretability to prevent safety drift while advancing robust AI systems.

You will collaborate with academic partners and global engineering teams to produce evaluative benchmarks and practical production-ready methods for next-generation trustworthy AI

Qualifications

• PhD or equivalent in CS or related field.
• 2+ years of experience leading a research agenda.
• Experience in ML safety, adversarial ML, and frontier AI evaluation.
• Publications in conferences/journals or repositories.

Responsibilities

• Drive foundational ML research on model robustness and interpretability.
• Develop evaluation protocols and benchmarks for frontier AI.
• Curate datasets and run fine-tuning to improve resilience.
• Collaborate with regional hubs, product teams, and academia to deploy concepts.
• Publish research in ML venues and participate in communities.

Skills

Research leadership

ML safety

Adversarial ML

Frontier AI evaluation

Publications

Education

PhD in CS or related field

Tools

Python

Job description

Senior Research Scientist, AI Safety and Security

Share Senior Research Scientist, AI Safety and Security

X Google will be prioritizing applicants who have a current right to work in Singapore, and do not require Google's sponsorship of a visa.

• PhD degree in Computer Science, a related field, or equivalent practical experience.
• 2 years of experience leading a research agenda.
  
• 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

In this role, you will join a specialized research effort dedicated to proactive threat mitigation, adversarial machine learning, and agentic security. Our team operates at trustworthy AI, focusing on securing next-generation AI models and intelligent agents against emerging threats. 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.

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy , Know your rights: workplace discrimination is illegal , Belonging at Google , and How we hire .

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

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Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.