YOLO & OpenCV Expert — Review AI-Generated Code

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. Creating an OpenTrain account is free and lets you discover projects, build a profile, and apply quickly.

We connect technical contributors with practical AI-training work — from code review to annotation and model evaluation — so you can help shape how cutting-edge AI behaves while working remotely and flexibly.

Why AI training matters

AI training (data labeling and human feedback) is the human side of building intelligence: people prepare, evaluate, and correct examples that modern models learn from. Contributors work across tasks like annotating images, reviewing model outputs, and evaluating code or technical explanations.

This role gives you direct influence on how AI provides technical guidance and code-level recommendations, with flexible part-time remote hours and work that impacts developers using OpenCV and YOLO in production.

The role

We are looking for a highly experienced OpenCV and YOLO developer to review AI-generated prompts, code, and responses focused on computer vision and real-time object detection.

As an AI interviewer and technical reviewer you will evaluate whether AI-generated code and explanations are technically accurate, efficient, and aligned with best practices, and you will provide clear, structured feedback and improved responses.

What you'll do

Your day-to-day work is reviewing AI outputs and assessing code snippets, explanations, and recommendations produced by models. Provide actionable feedback and rewritten guidance that developers can apply in real-world projects.

• Verify correctness of OpenCV functions and usage patterns in AI-generated code.
• Assess YOLO model designs (v4, v5, v7, v8), training scripts, and dataset preprocessing for errors and improvement opportunities.
• Evaluate performance and real-time inference strategies including CUDA, TensorRT, ONNX, pruning, and quantization recommendations.
• Check image preprocessing, feature extraction, bounding box regression, anchor/anchor-free usage, and non-maximum suppression correctness.
• Provide concise, well-structured code review comments and rewritten AI responses that align with best practices.
• Score or flag AI responses for technical accuracy, missing context, or misleading guidance.

Requirements

The job description requests strong, hands-on experience and domain knowledge — preserve these exactly when applying.

Note: The structured listing indicates 'Entry level' for experience level, while the detailed description requests at least 5+ years of hands-on experience. Please ensure your application clearly states your actual experience level.

• At least 5+ years hands-on experience in computer vision, deep learning, and real-time object detection (as stated in the role description).
• Expertise with OpenCV for image processing and YOLO (v4, v5, v7, v8) model training, evaluation, and deployment.
• Strong familiarity with deep learning frameworks such as PyTorch and/or TensorFlow.
• Experience optimizing inference performance using CUDA, TensorRT, ONNX, pruning, and quantization techniques.
• Solid knowledge of image preprocessing, feature extraction, bounding box regression, NMS, and dataset preparation.
• Excellent English writing skills for clear, structured feedback and documentation.
• Experience with code reviews, debugging, and technical documentation is a plus.

Interview format & instructions (AI interviewer role)

You will act as an AI interviewer assessing candidates' OpenCV and YOLO expertise and their ability to evaluate AI-generated code and responses. Use a professional yet engaging tone to keep candidates comfortable while ensuring objective evaluation.

Follow the structured flow below when conducting interviews or writing evaluation tasks.

• Introduction: Greet the candidate, explain the interview flow, and state that the session assesses OpenCV/YOLO expertise, AI-response evaluation skills, and communication.
• Technical questions: Ask about projects using OpenCV or YOLO; probe knowledge of YOLO architectures, training, dataset preprocessing, and real-time detection techniques.
• Optimization: Ask about quantization, TensorRT/ONNX acceleration, pruning, and hardware-specific optimizations (CUDA).
• Code review task: Present an OpenCV/YolO code snippet and ask the candidate to identify errors, inefficiencies, and fixes.
• AI response evaluation: Provide an AI-generated answer about OpenCV/YOLO and ask the candidate to critique its accuracy, clarity, and best-practice alignment, then rewrite an improved response.
• Communication task: Request a beginner-friendly explanation of a complex concept (e.g., NMS, anchor boxes) and a concise code-review comment for a provided snippet.
• Closing: Ask if the candidate has questions, thank them, and explain next steps.

Logistics, pay, and how it works

This is contract, part-time work (less than 20 hours/week) and is open to contributors worldwide. The role is paid hourly at the posted rate.

Exact pay and contract terms from the listing: PAY_PER_HOUR at USD 30/hour. The project uses OTHER labeling software and focuses on COMPUTER_CODE_PROGRAMMING label types.

• Employment types: Contractor, Part-time.
• Time requirement: Less than 20 hours per week.
• Pay: $30 USD per hour (PAY_PER_HOUR).
• Data type: COMPUTER_CODE_PROGRAMMING; Label types: COMPUTER_PROGRAMMING_CODING.
• Worldwide candidates accepted; excellent written English required for review tasks.
• How to apply: Create a free OpenTrain account, build your profile, and submit your application for this project.