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The landscape of artificial intelligence has shifted dramatically from the era of isolated academic curiosity to an industrial arms race driven by "frontier labs"—organizations like OpenAI, Google DeepMind, and Anthropic that operate at the cutting edge of capability. For a university student aspiring to join these ranks, the standard "data science" curriculum is no longer sufficient. The modern Research Engineer (RE) at a frontier lab is a hybrid archetype: part mathematician, part systems engineer, and part experimental scientist. They must possess the theoretical intuition to diagnose why a loss curve is diverging and the systems-level expertise to implement a fix across a cluster of thousands of GPUs.

This report outlines a rigorous, exhaustive, and self-directed curriculum designed to bridge the gap between a student with basic software knowledge and a candidate capable of contributing to the development of next-generation foundation models. This is not a path of least resistance; it is a path of maximum depth. The curriculum prioritizes "first-principles" understanding over high-level API usage. While it is possible to train a classifier in three lines of Python, doing so without understanding the underlying calculus, optimization dynamics, and memory hierarchy renders one incapable of pushing the boundary of what is possible. As identified in industry analyses, the most successful research engineers often hold advanced degrees or equivalent self-study depth in computer science, mathematics, and physics.