Role Overview
Join a forward-thinking initiative focused on enhancing artificial intelligence systems that process and understand visual information. In this role, you will play a key part in evaluating how well computer vision models perform across real-world applications, identifying weaknesses, and guiding improvements through data refinement and technical insight.
Key Responsibilities
- Review AI outputs for tasks including image classification, object detection, segmentation, and visual reasoning
- Analyze model predictions for accuracy, consistency, and resilience across diverse conditions
- Detect limitations, edge cases, and potential biases in visual AI behavior
- Support the development of high-quality annotated datasets for training computer vision systems
- Deliver clear technical assessments with actionable recommendations
- Apply your expertise across multiple domains such as medical imaging, self-driving systems, and document interpretation
Required Qualifications
- Proven understanding of core computer vision concepts like object detection, semantic segmentation, pose estimation, or image classification
- Direct experience using deep learning frameworks including PyTorch, TensorFlow, or JAX
- Familiarity with modern architectures such as convolutional neural networks, Vision Transformers, and diffusion models
- Ability to read and interpret current machine learning research
- Knowledge of best practices in visual data preprocessing, augmentation, and annotation
- Strong attention to detail when reviewing model behavior
- Clear and effective written communication in English
- Self-directed work ethic, especially in independent settings
- Verification of identity and eligibility to work as an independent contractor in your country
Preferred Qualifications
- Advanced degree in Computer Science, Electrical Engineering, or related field with focus on vision or machine learning
- Publication record in leading conferences such as CVPR, ICCV, ECCV, NeurIPS, or ICML
- Experience in 3D vision, video analysis, generative modeling, or multimodal AI systems
- Exposure to MLOps tools, experiment tracking, or model evaluation workflows
- Background in specialized areas like medical imaging, remote sensing, robotics, or autonomous vehicles
- Hands-on experience with large-scale annotation platforms and data quality pipelines
- Proficiency in Python and libraries including NumPy, OpenCV, and scikit-learn
Technical Environment
PyTorch, TensorFlow, JAX, CNNs, Vision Transformers, diffusion models, Python, NumPy, OpenCV, scikit-learn
Work & Compensation
- Fully remote position — work from any approved location
- Flexible weekly hours, typically ranging from 15 to over 40 hours depending on project needs
- Compensation ranges from $30 to $70 per hour, with most projects billed at $30/hour, adjusted based on experience and region
- Weekly payments processed via PayPal or Stripe
