OpenAI Cookbook Developer — Code Review & Evaluation

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help people start and grow careers teaching AI: discover projects across the industry, build a profile, and apply in minutes. Creating an OpenTrain account is free.

Why AI Training Work Matters

AI training (data labeling/annotation/human feedback) is the human side of building artificial intelligence. Contributors annotate, review, and evaluate model outputs so modern AI systems behave reliably and safely.

This work is usually 100% remote, highly flexible, and accessible: many projects need strong attention to detail and communication rather than formal credentials. You’ll be on the cutting edge, shaping how models behave by providing precise technical feedback.

The Role — What You’ll Do

You will analyze AI-generated prompts, code snippets, and explanations that implement patterns from the OpenAI Cookbook and label/categorize them for quality, correctness, efficiency, and adherence to best practices. Your feedback must be structured, actionable, and written in clear English.

A distinct part of this engagement is conducting AI-driven technical interviews for candidates who apply to work as OpenAI Cookbook developers on related projects. You will assess candidates’ hands-on experience, debugging ability, prompt engineering skills, and clarity of communication using the interview guidelines provided.

• Review AI-generated OpenAI API implementations and explanations for correctness and efficiency.
• Label and categorize code snippets and textual explanations using provided annotation schemas.
• Identify errors, inefficiencies, or missing optimizations and recommend improvements.
• Run focused technical interview scenarios to validate candidates’ real-world OpenAI Cookbook experience.
• Provide structured written feedback suitable for developer audiences.

Typical Tasks & Example Checks

You’ll perform hands-on review and annotation tasks that may include debugging API calls, evaluating prompt and tokenization strategies, checking embedding usage, and assessing model deployment recommendations. Reviews should reference OpenAI Cookbook best practices where applicable.

Interview tasks will use practical prompts and snippets to probe depth of knowledge. Example snippet you may present to a candidate for debugging: import openai response = openai.Completion.create( model="text-davinci-003", prompt="Translate this into French: 'Hello, how are you?'", max_tokens=0 ) print(response) — ask them to identify the issue and suggest optimizations.

• Evaluate statements like: 'To fine-tune GPT-4, you can upload your dataset and train the model using OpenAI’s public API.' — ask candidates how they would correct or clarify this.
• Test candidates on trade-offs: fine-tuning vs embeddings, token-use reduction, cost vs quality optimizations.
• Require concise, structured explanations suitable for developer documentation or code review comments.

Requirements

The hiring team expects strong, demonstrable hands-on experience with OpenAI’s API and Cookbook patterns. Please note the description requests 5+ years of hands-on experience working with OpenAI’s API, fine-tuning models, prompt engineering, embeddings, tokenization strategies, API optimizations, and model deployment.

You must also have strong English writing skills because the role requires producing clear, structured feedback and conducting technical interviews in English.

• 5+ years of hands-on OpenAI API experience (fine-tuning, prompt engineering, embeddings, tokenization, deployment).
• Proven ability to read, debug, and optimize OpenAI API code and explain corrections clearly in English.
• Experience evaluating AI-generated code or model outputs and producing structured review notes.
• Availability for less than 20 hours per week; remote work from any country is acceptable.

Who Should Apply

Experienced developers, ML engineers, or technical evaluators who have applied techniques from the OpenAI Cookbook to real projects and who enjoy code review, debugging, and mentoring are a strong fit.

You should be comfortable probing candidates for concrete examples, asking follow-ups to expose depth, and rejecting answers that are only theoretical rather than hands-on.

• You enjoy structured technical assessment and can convert observations into clear, actionable feedback.
• You can run interviews that probe practical problem-solving and optimized API usage.

Compensation, Time Commitment & Logistics

This contract, part-time role pays $20 USD per hour and is billed per hour. The expected time commitment is less than 20 hours per week. Work is contractor-based and fully remote; candidates from any country may apply.

Labeling tasks will focus on computer code/programming (annotation type: COMPUTER_PROGRAMMING_CODING) and use custom or other labeling software provided by the project. You will follow provided annotation schemas and interview scripts.

• Pay: $20 USD per hour (PAY_PER_HOUR).
• Time requirement: Less than 20 hours/week; contract, part-time engagement.
• Labeling focus: Computer code / programming;

How the Interview & Review Process Works

You will be given interview guidelines and example checks to follow: probe for specific projects and solutions, present buggy code snippets for debugging, ask candidates to explain tokenization and when to use fine-tuning versus embeddings, and require concise corrections to inaccurate model guidance.

When reviewing AI-generated content, produce labeled outputs and a short justification for each label that explains the issue, references the applicable Cookbook best practice, and suggests a fix or improvement.

• Probe for depth: ask follow-ups if answers are surface-level.
• Reject candidates lacking hands-on experience or failing real-world debugging tasks.
• Deliver structured feedback: label, short justification, and suggested remediation for each item.