Remote AI Trainer

• Engagement: Contract, paid hourly. No 40-hour minimum — work arrives in sprints, and you take on

what suits you.

• Start: Immediately. The client has sample work that needs turning around this week, with a

substantially larger programme expected to follow.

About the work

Our client builds high-fidelity datasets used to train and evaluate frontier large language models. One of

the things the AI labs want most right now is software engineering tasks for reinforcement learning

environments — specifically, tasks that frontier models cannot solve.

The RL environments are already built. What the client needs is a steady supply of tasks to run inside

them, written by engineers who know what hard, real work actually looks like.

What you’ll be doing

• Writing realistic software engineering tasks: go into a repository, change or fix something, apply a

patch, clean up afterwards. The emphasis is on work an engineer would genuinely be paid to do not competition-style puzzles or textbook exercises.

• Writing the instructions, test cases and expected outputs tightly enough that there is exactly one

correct interpretation.

• Running frontier models against your tasks and evaluating the responses against rigorous functional

and logical standards.

• Where your expertise sits outside Python, helping build out the RL environment for that language.

The bar

A good task defeats a frontier model on the merits: the model understood exactly what was being asked

and still could not do it.

A task that "wins" because the wording was loose does not count. If a model returns something you

weren’t looking for because you didn’t specify properly, that is a fault in the task, not in the model.

Holding that line — hard, but scrupulously unambiguous — is most of the job.

What the client is looking for

• Real engineering depth. The kind that comes from shipping and maintaining production systems.

Years on a CV matter less than the quality of your judgement; some of the client’s strongest

contributors are very young.

• Python, or a very good reason not to. Python is the primary environment. But the client is language

agnostic: if you have spent a career in Java, C++ or COBOL and never picked up Python, that

expertise is genuinely valuable — there is real demand for tasks that move legacy code into modern

languages.

• Intellectual honesty. You say so when you don’t know something, you raise problems while they are

still fixable, and when you say you’re nearly done, you’re nearly done. This matters more than

almost anything else.

• Clear written English. Nearly everything you produce is written specification that someone else has

to be able to read without asking you a question.

Helpful, but not required

• Prior work on AI data platforms — Outlier, Alignerr, Scale, Surge or similar RL and data-annotation

workflows.

• Experience across several codebases, stacks or domains rather than one.