I've worked remotely on AI projects with teams across 7 countries.

Here's what's different about remote ML collaboration compared to remote software development.

The big challenge: ML experiments are harder to communicate asynchronously. "I tried X and it didn't work" tells your teammate nothing. They need to see the data, the metrics, the failed experiment details.

What makes remote ML work: obsessive experiment logging. Every experiment in W&B or MLflow with full configs, metrics, and notes. When I say "approach A didn't work," there's a link my teammate can click to see exactly what happened.

Shared computational resources need clear protocols. Nothing kills remote productivity like "I can't train because someone else is using the GPU cluster and I don't know when they'll be done."

Documentation becomes 10x more important. That architectural decision you'd explain verbally in an office? Write it down. The preprocessing quirk you'd show on a whiteboard? Document it.

And async communication with clear structure: what I tried, what I observed, what I recommend, what I need from you.

Remote ML work is absolutely viable and increasingly common. But it requires more intentional communication than collocated teams. The teams that invest in communication infrastructure outperform teams with better engineers but worse coordination.