ML Engineer Roadmap: Complete 2026 Study Plan

The ML Engineer track is for engineers who want to own models end-to-end, not just call them. You will train transformers from scratch, run distributed jobs across GPU clusters, fine-tune foundation models with LoRA, build feature stores, set up A/B testing for model improvements, and deploy serving systems that handle real traffic. The math is not optional here — you will read loss surfaces, reason about optimiser internals, and debug training instabilities that no tutorial prepared you for. At the end of 24 weeks you will have a portfolio that demonstrates you can ship models with measurable business impact, which is the sentence that gets ML Engineer roles at hyperscalers and frontier labs.