Transformers Developer, Code Evaluation & AI Review

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We connect skilled contributors with projects that shape how AI systems learn and behave. Creating an OpenTrain account is free.

For this role OpenTrain is the hiring organization: you'll work as a contractor on AI evaluation tasks that directly improve model outputs, code examples, and documentation used across AI systems.

• Remote-first, flexible contract work you can do from anywhere.
• Work directly on the human side of AI — analyzing and improving model outputs, code, and guidance.

About AI Training Work

AI training (data labeling / human feedback) is the essential human component behind modern AI. Contributors annotate, review, and rate model outputs so models learn correct behavior.

This line of work is highly flexible and accessible: many projects allow part-time schedules and require practical domain knowledge or language skills rather than formal degrees.

• Great for experienced engineers who want flexible, remote, part-time work.
• You’ll directly influence how Transformer-based systems are explained, fine-tuned, and deployed.

The Role

We need an experienced Transformers developer to evaluate AI-generated Transformers code and explanations, and to conduct technical interviews that screen other Transformers candidates for an AI evaluation project.

This is a contractor, part-time role (less than 20 hours/week) paid at $27 USD per hour. Work includes labeling and categorizing AI outputs, identifying inaccuracies or inefficiencies in code, and producing structured, actionable feedback in clear English.

• Analyze AI-generated prompts, code snippets, and explanations for correctness, relevance, and best practices.
• Run technical interviews using provided guidelines to verify candidates' hands-on experience with Hugging Face and Transformers.
• Provide detailed written feedback that improves model guidance, documentation, and code quality.

What You'll Do Day-to-Day

You will review AI-generated Transformer examples and implementations, label outputs, and write concise remediation suggestions. You will also administer technical interview scenarios and evaluate candidate responses against a provided rubric.

• Identify errors, misconceptions, or inefficiencies in Transformer code and explanations.
• Recommend fixes and optimizations (tokenization choices, fine-tuning strategies, inference performance tips).
• Run code-debugging exercises and judge candidate fixes for correctness and practicality.
• Produce structured evaluation notes and labels for each reviewed example or candidate.

Requirements

All substantive requirements come from the role description; please ensure you meet them before applying.

• 5+ years hands-on experience using Hugging Face Transformers (fine-tuning, model hub, tokenization).
• Deep NLP and Transformer knowledge: attention mechanisms, tokenizers, fine-tuning workflows, and inference optimization.
• Proven ability to debug Transformer code, interpret model outputs, and suggest practical fixes.
• Strong English writing skills — you will produce structured, detailed feedback and candidate assessments.
• Available to work up to 20 hours/week; this is a contract, part-time role paid at $27 USD/hour.
• Worldwide applicants accepted; you must be able to complete evaluation tasks and interviews in clear English.

Interview & Evaluation Process

You will follow a provided AI-driven interview rubric to probe candidates’ real-world Transformer experience and debugging ability. Expect to present code snippets with errors, evaluate candidate explanations of core concepts, and judge their written feedback clarity.

The role emphasizes hands-on debugging and practical answers over theory. You should probe for depth, request concrete examples, and reject responses that lack real-world implementation details.

• Use supplied errorful code snippets to test debugging and optimization skills.
• Assess candidates on fine-tuning, tokenization strategy, inference speedups, and deployment considerations.
• Score and label AI-generated responses for correctness and helpfulness, and document recommended corrections clearly.

How To Apply

Create an OpenTrain account (free), complete your profile, and submit an application for this listing. Include links or descriptions of Transformers projects, code samples or GitHub repos, and a short note about your availability.

We will review applications and invite qualified candidates to a short onboarding and calibration task that demonstrates the evaluation workflow.

• Application materials we look for: project descriptions, code samples, and evidence of real-world Transformer deployments.
• Successful applicants proceed to a paid calibration task to confirm fit and alignment with evaluation standards.