Research Scientist, AI Safety and Security

• 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

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.

• 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.).