Initiate and lead novel research directions, applying insights from machine learning and related fields (e.g., NLP, Reinforcement Learning (RL), computational neuroscience) to advance long-context attention mechanisms, Retrieval-Augmented Generation (RAG), continual learning architectures, and multi-step reasoning frameworks. Design and execute end-to-end experiments, proposing and testing hypotheses to better align model-based agents, mitigate catastrophic forgetting, and improve sample efficiency in non-stationary environments.
Develop evaluations and benchmarks that stress-test long-horizon memory, out-of-distribution generalization, and complex planning capabilities, including in-depth search debugging of failure modes. Build and improve infrastructure for high-capacity context windows, dynamic memory structures, and continuous training pipelines in close collaboration with development teams. Communicate research findings clearly through plots, writeups, and paper-ready narratives, while contributing to a team culture of first-principles thinking, high standards, and constructive feedback.
Job description provided by Google.
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