YOLOv7 Code Review & AI-Generated Output Evaluator

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 by connecting contributors with projects, building profiles, and enabling quick applications. Creating an OpenTrain account is free.

About AI training work

AI training (aka data labeling, annotation, or human feedback) is the human side of building modern AI systems. Contributors annotate, review, and evaluate model outputs—work that directly shapes how state-of-the-art models behave.

This role sits at the intersection of model engineering and human evaluation: you will review AI-generated code and explanations so developers and other contributors get reliable, production-ready guidance.

The role

You will act as an AI interviewer and expert reviewer for AI-generated prompts, explanations, and code related to YOLOv7 and real-time object detection. Your primary goal is to evaluate technical accuracy, point out errors or inefficiencies, and produce clear, actionable feedback so the AI improves its outputs.

Work is contract, part-time, remote, and designed for contributors who can commit less than 20 hours per week.

What you'll do

Use your YOLOv7 and deep learning expertise to assess AI-generated material and candidate responses during structured interviews. Provide precise, well-written feedback and suggest improvements that reflect real-world best practices.

• Review AI-generated prompts, explanations, and YOLOv7 code snippets for correctness and efficiency.
• Verify model training details: dataset preprocessing, augmentation, anchor box tuning, batch size, and loss considerations.
• Evaluate model performance claims using metrics like mAP, IoU, and FPS and assess inference speed trade-offs.
• Assess deployment feasibility and optimization strategies (TensorRT, ONNX, OpenVINO, model quantization).
• Identify bugs, inefficiencies, or missing context in code and provide concise, actionable code-review comments.
• Deliver clear written feedback in excellent English that the AI can use to improve future outputs.
• Conduct structured interview interactions that probe technical depth, problem solving, and communication clarity.

Requirements

Preserve all substantive constraints from the task description: the role requires strong hands-on experience and the ability to communicate technical feedback clearly in English.

• Minimum 5+ years hands-on experience in object detection, deep learning, and real-time computer vision (explicitly required).
• Deep practical knowledge of YOLOv7: training, fine-tuning, architecture, and improvements over prior YOLO versions.
• Strong PyTorch experience and familiarity with common image augmentation techniques.
• Experience with anchor box optimization, model quantization, and inference acceleration workflows.
• Experience deploying models on edge/production using TensorRT, ONNX, or OpenVINO.
• Solid understanding of evaluation metrics and trade-offs: mAP, IoU, FPS, and real-world inference constraints.
• Excellent English writing skills for structured, detailed technical feedback.
• Prior experience with code reviews, debugging, or documentation is a plus.

Interview format & instructions

You will follow a structured, professional-but-engaging interview script that tests both technical skill and the ability to evaluate AI outputs. The interviewer role and tone are part of the deliverable: greet candidates, explain the process, and encourage detailed responses while maintaining objectivity.

• Begin by greeting the candidate and explaining the interview goals: assess YOLOv7 depth, AI response evaluation, and communication.
• Ask about hands-on projects using YOLOv7 and probe architecture differences versus earlier YOLO versions.
• Test training strategies: dataset prep, augmentations, anchor tuning, batch size, and loss tweaks.
• Present code snippets for error-spotting and optimization suggestions; request concise code-review comments.
• Provide AI-generated responses related to YOLOv7 and ask the candidate to identify inaccuracies, missing context, and propose improved rewrites.
• Ask the candidate to explain complex concepts simply (e.g., NMS, decoupled heads, confidence thresholding).
• Close with time for candidate questions and explain next steps.

Logistics, pay, and how to apply

This is a contract, part-time position open globally and fully remote. Expect to work less than 20 hours per week. The listed hourly rate is USD 30 per hour. Candidates must be able to communicate fluently in English in writing.

To apply, submit a brief cover note describing your YOLOv7 experience, a link to relevant code or projects (if available), and confirmation you meet the 5+ year experience requirement.

• Employment type: Contractor, Part-time.
• Hours: Less than 20 hours/week, flexible scheduling.
• Pay: $30 per hour (USD).
• Worldwide applicants welcome; strong written English required.