Agentic Reasoning ML Researcher

We are looking for a passionate and motivated researcher with a solid track record of solving challenging problems, advancing state-of-the-art, and demonstrated passion for ground-breaking research that could be scaled to production environment with an emphasis on the intersection of AI and Safety.

Your responsibilities:

• Design and implement effective, efficient and novel architecture to enable AI detection system that understands and able to reason over complex multimodal data, such as images, videos, text and cultural knowledge.
• Explore novel techniques for visual question answering, visual reasoning, text-based reasoning, and multimodal knowledge grounding. Explore the use of knowledge graphs and other neural-symbolic techniques to enhance reasoning capabilities.
• Create new techniques to make AI models/Agentic AI system more interpretable, unbiased, transparent and aligned with human values for real-world scenarios.
• Create new techniques to ensure consistent alignment between user-centric explanations and intrinsic model behavior. Develop and implement state-of-the-art alignment techniques (e.g., RLHF, RLAIF, Constitutional AI) specifically tailored for LLMs CoT, agentic workflows and multi-step reasoning.
• Reverse-engineer on neural networks to understand internal workings of AI models rather than treating them as "black boxes". Implement and integrate AI explainbility techniques and tools.
• Develop data attribution methods to quantify the influence of specific data points (text and image) on a model's prediction and implement solution.
• Build rigorous benchmarks and datasets to measure the quality of reasoning capabilities and reliability, quality of user-centric explanations, model transparency, and effectiveness of treatments.
• Develop robust defenses against jailbreaking, prompt injections, and adversarial exploits that target a model’s planning and tool-use capabilities.
• Communicate insights to stakeholders.
• Turn cutting-edge AI detections and AI safety papers into high-performance, scalable code, transforming theoretical breakthroughs into production-ready tools and frameworks

Requirements:

• Ph.D. in Computer Science, Deep Learning, Machine Learning, Mathematics or other related fields.
• Focused on research in the AI field with good track records and high motivation in Explainable AI and Safety domain.
• Strong proficiency in Large Language Models (LLMs)/VLM, neural networks, neuro-symbolic AI, graph reasoning architectures.
• Strong background in Python, Java, or C++, with deep knowledge of ML frameworks such as PyTorch, TensorFlow.
• Successful experience in AI alignment to human values and expectations, model robustness improvement, controlled and continual learning, neural network interpretability and editing techniques is highly valued.
• Pioneering novel methods and neural networks that revolutionized machine learning or the AI field, or revolutionized the industry, is a big bonus.
• Strong publication record in top conferences (i.e. NeurIPS/ICLR/ICML/ AAAI/ACL/CVPR/ICCV/EMNLP/NAACL).
• Candidates with ICPC, IOI/IMO, IOAI and other international competition in computer science, machine learning, and AI, etc. are highly preferred.
• Good teamwork, enjoy working with multi‑culture teams, passionate in challenging the status quo.