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XGoogle 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:
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
