The gap between AI research and AI products is the most expensive gap in the tech industry.

And the engineers who bridge it are worth their weight in gold.

The problem: research teams produce papers with impressive results on benchmarks. Product teams need systems that work reliably, at scale, within budget, on real data. The translation from one to the other often fails.

Research says: "Our model achieves SOTA on MMLU."
Product asks: "Can it handle 10,000 concurrent users with p99 latency under 200ms and cost less than $0.005 per query?"

These are completely different questions requiring completely different skills.

The Research-to-Product Engineer: understands research papers deeply enough to identify what's practical. Can prototype research ideas quickly. Knows how to evaluate whether a research result will survive contact with real data. Can engineer the gap between "works in a notebook" and "works in production."

This role is called different things at different companies: Applied ML Scientist, ML Engineering Lead, Staff ML Engineer. But the skill set is the same: deep technical understanding + pragmatic engineering + product sense.

Compensation: $200K-$400K+ because these people directly translate research investment into product revenue.

If you're a researcher frustrated that your work never ships, or an engineer frustrated that research never works in practice — this bridge role is where you should aim.

The translation layer is where the most value is created in AI. And it needs more people.