Human Feedback for Python Code Generation

About OpenTrain

OpenTrain is the #1 platform for people starting and growing careers in AI training and data labeling. The platform connects contributors with projects that shape how modern AI systems behave — from annotating images to providing human feedback that guides model outputs. Creating an OpenTrain account is free.

About AI training and RLHF

AI training (data labeling/annotation) is the human side of building modern AI. Reinforcement Learning with Human Feedback (RLHF) uses reviewer judgments, corrections, and rankings to teach models how to produce better outputs. Contributors do real work that directly improves model accuracy, safety, and usefulness.

• Work is typically 100% remote and flexible.
• Many projects require no prior experience; domain knowledge can increase pay.
• This project focuses on code-generation tasks for Python, a high-impact area in AI development.

The Role

You will provide human feedback on auto-generated Python code to improve how large language models write scripts, functions, and algorithms. Tasks include reviewing, correcting, optimizing, and explaining code so models learn coding standards, efficient practices, and problem-solving strategies.

• Project type: RLHF for Python code generation.
• Employment: Contractor.
• Data type: Computer code / programming.
• Labeling software: Scale AI.

What You'll Do

Daily work consists of short review tasks delivered through the Scale AI interface. You will evaluate model-generated Python snippets, apply fixes, suggest improvements, and sometimes rewrite functions for clarity or performance. Clear, well-documented corrections are essential so training data is useful to models.

• Review and validate auto-generated Python scripts and functions.
• Correct syntax, logic errors, and edge-case handling.
• Refactor and optimize code for readability and efficiency.
• Add concise comments or explanations when requested to clarify intent.

Requirements

This is an entry-level role aimed at people comfortable reading and editing Python code. You do not need formal credentials, but you should be confident identifying bugs, understanding basic algorithms, and writing clean Python. There are no additional mandatory requirements listed for this project.

• Familiarity with Python programming (reading and editing) is required.
• Attention to detail and ability to explain changes clearly.
• Reliable internet access and a device capable of using Scale AI's web interface.
• Open to worldwide applicants; work is 100% remote.

Payment, Schedule, and Logistics

Compensation is hourly at 50 USD per hour (PAY_PER_HOUR). You will be engaged as a contractor and paid according to the project's payment schedule. Tasks are generally flexible and can be completed on your own schedule; specific time commitments are set by the project when you start.

• Pay: 50 USD per hour.
• Type: Contractor (no employment benefits implied).
• Labeling tool: Scale AI platform.
• Worldwide applicants are accepted.

Who Should Apply

Apply if you read Python comfortably and enjoy code review and problem solving. This project is a good fit for students, early-career developers, QA engineers, or anyone looking to break into AI training and help shape how coding assistants behave.

• Entry-level friendly — practical Python experience matters more than formal titles.
• Ideal for people seeking flexible, part-time remote work.
• Contributors will directly influence model quality used in real-world coding tools.

How It Works

To apply, create a free OpenTrain account and submit for this project. If accepted, you'll receive instructions for accessing tasks in Scale AI, along with guidelines and examples. Complete reviews, submit corrections, and optionally provide brief rationales where requested — your labeled examples become training data for improving Python code generation.

• Sign up on OpenTrain to apply and see project details.
• Accepted contributors are given access to Scale AI tasks and project guidelines.
• Work asynchronously and submit annotations through the Scale AI interface.