Moving past basic algorithms, 2026 ML fundamentals prioritize model evaluation metrics, statistical grounding for Generative AI, fine-tuning methodologies, and
Machine learning and evaluation fundamentals, training/validation discipline, metrics, error analysis, bias detection, are the judgment layer of the AI era. Even teams that never train models need people who can measure them honestly: the eval mindset is classic ML rigor applied to LLM systems.
Durable: while training jobs concentrate in fewer teams, evaluation rigor is exploding in demand, every AI feature now needs someone who can prove whether it works.
The discipline more than the algorithms: validation rigor, metric literacy, and error analysis transfer directly to LLM evaluation, and tabular problems still favor classic methods on cost and accuracy.
Become the evaluation person: build eval harnesses for LLM features with statistical honesty. It's scarce, immediately useful, and the on-ramp to every senior AI role.