We are looking for a PhD-level research scientist to help define the scientific foundations of test-time cognition for LLMs and agentic AI systems. You will develop new algorithms for adaptive inference, uncertainty-aware compute allocation, search, verification, and multi-model cognitive orchestration. Our core thesis is that the next major gains in AI will come not only from larger models or more training, but from organizing cognition dynamically at test time under real-world resource constraints.