Hire LLM engineers remotely from China

The labs that make this pool different

Very few countries contain engineers who have trained a frontier language model. China contains several teams of them, and they contract. DeepSeek, Alibaba’s Qwen group, Moonshot AI, Zhipu AI, MiniMax and ByteDance have each shipped open-weight model families that are downloaded and cited worldwide.

What that buys you is experience that is genuinely scarce anywhere: pretraining runs measured in trillions of tokens, distributed training across thousands of accelerators, serving at consumer volume, and the evaluation discipline that stops a model regressing quietly after a change that looked fine. If you are fine-tuning an open-weight model today, there is a fair chance it came out of one of those teams.

Three profiles you will actually see

In Britain the people who have done that work sit in a handful of labs that pay accordingly, and you are bidding against your competitors for the same names. Widening the search does not lower the bar — it changes who is available at all. In practice the shortlists divide into three:

• Ex-lab engineers who worked on a named open-weight model and can point you at the paper, the model card and their commits.
• Platform engineers from the large consumer companies, who have served language models at volumes almost nobody here has touched and treat cost per thousand requests as a design input rather than a surprise.
• Applied engineers who have shipped user-facing features on open models for two or three years. The most common need, and where the gap between British and Chinese supply is widest.

Be explicit about which you want before you brief the role. Hiring a pretraining specialist to build a retrieval feature wastes them and frustrates you.

This is the rare role where you can check the claims

Most engineering hires come down to trusting a CV and an interview. Here a great deal of the relevant work is public, which changes the screen entirely. Ask for the Hugging Face handle and the GitHub profile, then actually read them: commits, model cards, issue threads, the paper’s author list.

Two cautions. Author lists on large model releases can run to dozens of names, so establish what the person did rather than counting papers. And an impressive public profile is not the same as having supported something under load — ask what broke after release. Where the record is thinner than you would like, our note on verifying Chinese engineering credentials covers what else is checkable.

Why a time difference matters less here than you might think

Much of this work is naturally asynchronous. You launch a training run, you wait, you measure. You build an evaluation set, you score last week’s change against it. Neither needs anybody in a room at four in the afternoon.

Four hours of overlap covers a standup and a review cycle, which is enough for most briefs; a training or evaluation brief often works on three. What does not survive a time difference is work that needs a product manager present every day to settle what “good” means — so settle that first, in writing, and the rest decomposes. Our note on overlap hours sets out what is sustainable rather than what is theoretically survivable.

Decide what the engineer can reach, before you start

Weights, training data and evaluation sets are the assets in this role, and access to them is a decision with legal weight rather than an IT ticket. It is much easier to design at the briefing stage than to unpick in month two.

The distinction that matters is between building on an existing model and advancing model capability: the second sits closer to the restricted end, and the analysis differs accordingly. Read the note on export controls and remote AI work before you scope anything that touches training rather than use. The constraint is on the work, not on the person, and narrowing a brief is usually all it takes.