Postdoctoral Researcher (Foundational AI: Theory and Architectures)

Develop an original research program that advances fundamental understanding or methodology in modern artificial intelligence. Formulate and investigate important research questions using theoretical analysis, algorithm or architecture design, empirical study, or an appropriate combination of approaches. Derive, design, implement, validate and iterate on new architectural through controlled and large-scale scaling experiments. Collaborate with researchers working on model efficiency, reasoning systems, learning algorithms, and large-scale experimentation. Pursue an independent research program and contribute to the team's broader scientific direction. Disseminate research through peer-reviewed publications, conference presentations, open scientific engagement, and collaboration with the broader research community. A PhD (completed or near completion) in Machine Learning, Computer Science, Mathematics, Statistics, or a related field. Expertise in one or more sub-fields of AI/ML, evidenced by top-tier publications and/or experience. A strong record of original, peer-reviewed research published at leading venues in AI, machine learning, or theory, such as NeurIPS, ICML, ICLR, COLT, STOC, FOCS, or SODA. Expertise in one or more areas relevant to theoretical and/or practical aspects of modern foundation models, including but not limited to: optimization and training dynamics, LLM architecture, representation learning, attention mechanisms, test-time computation or training, reasoning, memory, adaptation, agentic interaction, efficiency, scaling or machine learning theory. Demonstrated ability to formulate, investigate, and iterate challenging research questions independently. Excellent written and verbal communication skills, with the ability to explain complex ideas to a broad scientific audience. Hands-on experience designing, implementing, training, and evaluating AI/ML models using a modern framework such as PyTorch or JAX.