AI Trainer: Code Generation

Overview

We are building a focused group of engineers to improve how large language models reason through real world code. This initiative centers on evaluating and refining multi step reasoning trajectories derived from real GitHub repositories, with the goal of producing higher quality, more reliable code generation outputs.

This is a long term project requiring strong engineering judgment rather than surface level labeling. Contributors will work directly with complex code paths and reasoning flows across multiple platforms.

What You Will Do

You will analyze and refine multi step code reasoning trajectories generated from real production repositories.

This includes:

• Reviewing model generated reasoning sequences

• Identifying logical inconsistencies or weak reasoning steps

• Improving trajectory structure to produce stronger, production grade outputs

• Evaluating reasoning quality across different programming environments

The work is closer to debugging model logic and reasoning systems than to traditional annotation tasks.