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ML research skills don't automatically transfer to production roles. Here's what changes, what transfers, and how to bridge the gap — from someone who's seen both sides.

Production ML interviews test systems thinking across five rounds — not just algorithms. Here's how to prep for every round with a production engineer's mindset and a four-week plan.

Your ML resume lists tools and models. Hiring managers want systems impact. Here's how to rewrite your resume around production outcomes that get callbacks.

Most ML interview prep focuses on algorithms. Here's what system design rounds actually test — and how production experience gives you an unfair advantage.

Skip the MNIST tutorials. Five production-grade projects mapped to the hiring signals real ML managers look for: observability, data engineering, experimentation, MLOps, and scale.

Five concrete strategies for production ML engineers to prove their work without violating NDAs. Sanitized docs, architecture diagrams, methodology posts, and more.

Why the best ML engineers have the worst portfolios. 8 years of FDA diagnostics, document AI, and multi-agent systems — all under NDA. Here's what to actually do about it.