Transformers (Hugging Face) Developer Needed for AI Code Evaluation

About OpenTrain

OpenTrain is a central job board for data-labeling and AI-training work. We aggregate opportunities from many AI companies and labeling platforms so candidates can discover relevant projects in one place instead of hunting across dozens of sites.

Creating an OpenTrain account is free, and applying takes only a few minutes. This listing is for a contractor, part-time role managed through the OpenTrain platform.

About AI Training Work

AI training (data labeling / human feedback) is the human side of building machine learning systems. People prepare, review, and grade examples — including code and explanations — so models learn to be accurate, safe, and useful.

This role focuses on Transformer models and the Hugging Face ecosystem: you will evaluate AI-generated Transformer code and explanations, helping improve model guidance, documentation, and real-world deployments.

The Role

We are seeking an experienced Transformers developer to evaluate AI-generated Transformers code and explanations, and to conduct AI-driven technical interviews for candidates applying to Transformers projects.

You will label, categorize, and assess model outputs for accuracy, relevance, and adherence to best practices; identify errors and inefficiencies in explanations and code snippets; and deliver clear, structured feedback that helps improve model outputs and developer guidance.

• Work type: Remote contractor, part-time.
• Time commitment: Less than 20 hours/week.
• Pay: $27.00 USD per hour (PAY_PER_HOUR).
• Worldwide applicants accepted.

What You'll Do

Your day-to-day combines technical evaluation and structured interviewing. You will review AI-generated prompts, code, and prose, then label and score outputs and provide actionable corrections and improvements.

• Analyze AI-generated Transformers code and explanations for correctness, efficiency, and best practices.
• Label and categorize outputs according to project guidelines and provide structured feedback.
• Run AI-driven interviews to assess candidate hands-on skills with Hugging Face Transformers.
• Present and debug Transformers code snippets; recommend fixes and optimization strategies.
• Identify tokenization, model-architecture, training-pipeline, and inference inefficiencies and explain improvements clearly.

Requirements

Applicants must meet the technical and communication expectations for evaluating Transformers work and interviewing candidates.

• Hands-on experience: 5+ years working with Hugging Face's Transformers library, fine-tuning pre-trained models, and deploying Transformer-based applications.
• Deep expertise in NLP, model training and evaluation, tokenization strategies, attention mechanisms, and inference optimization.
• Strong English writing skills — required for writing precise, structured feedback and for conducting interviews.
• Prior experience evaluating AI-generated code or documentation is expected.
• Availability to perform labeling and interview tasks within a part-time contractor schedule (under 20 hrs/week).
• Employment type: Contractor / Part-time.
• Data type: COMPUTER_CODE_PROGRAMMING; Label types: COMPUTER_PROGRAMMING_CODING; Labeling software: OTHER.
• Note on experience level: the posting metadata lists 'Entry level' but the role requires 5+ years of hands-on Transformers experience; applicants must meet the 5+ years requirement.

Interview & Evaluation Guidelines

You will follow a structured interview and evaluation process to screen candidates and to provide labels/feedback on AI-generated content. Focus on depth, hands-on proof, and clear communication.

• Experience assessment: Ask candidates to describe real-world Transformer projects (fine-tuning BERT/GPT/T5/Whisper, Model Hub usage, deployments). Probe for concrete challenges and solutions.
• Technical checks: Present a broken Transformers code snippet and ask them to identify and fix issues; ask how they'd fine-tune for a specific task and how they'd optimize inference.
• Evaluation tasks: Have candidates assess AI-generated statements (e.g., correct the claim 'BERT is an autoregressive model designed for text generation') and implementations, then suggest improvements with reasoning.
• Communication checks: Require candidates to explain core Transformer functionality in simple terms and describe real-world applications clearly.
• Screening rules: Reject candidates with only theoretical knowledge, those who cannot debug real code, who provide generic answers, or whose written explanations are unclear or unstructured.
• Final confirmation: Verify prior experience evaluating AI-generated code, confirm availability, and ensure readiness to perform precise labeling tasks.

How to Apply

Apply via OpenTrain. Creating an account is free and the application process takes only a few minutes.

When you apply, be prepared to demonstrate your Transformers experience and to participate in a technical evaluation that includes code review and a short candidate-interview simulation.

• Be ready to show concrete projects, repositories, or examples of fine-tuning and deployment work.
• Expect a practical debugging exercise and a short evaluation of AI-generated Transformer outputs.
• Successful applicants must be able to produce clear, structured written feedback in English as part of the role.