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September 18, 2025 · 8 min read
Evaluation before accuracy
Applied ML internships at Exprosoft start with a frozen eval set, not a Kaggle instinct.
Most student ML work collapses at the same point: the benchmark metric moves slightly, but nobody can say whether the system got better or worse for the human operator on edge cases.
Our applied ML internships start with a frozen evaluation set, a written decision of what “wrong” costs in production, and a queue that records disagreement between human reviewers and model inference. The model is a suggestion engine; the desk remains accountable.
If you want to intern on applied ML at Exprosoft, expect more time on error analysis, data contracts, and calibration than on tuning transformer hyperparameters in isolation.