Computer Vision & ML Expert

About The Role

What if your expertise in computer vision could directly shape how the next generation of AI systems perceives, interprets, and understands the visual world? We're looking for Computer Vision & Machine Learning Experts to evaluate, improve, and guide AI models that process images, video, and visual data — helping build systems that truly see. This is a fully remote, flexible contract role for practitioners with hands-on experience in computer vision and machine learning. Whether you're a researcher, engineer, or advanced graduate student, your technical depth will have a direct, measurable impact on AI systems used by millions.

Who You Are

• Strong foundational knowledge of computer vision — object detection, image classification, semantic segmentation, pose estimation, or related areas
• Hands-on experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX
• Familiarity with common CV architectures (CNNs, Vision Transformers, diffusion models, etc.)
• Comfortable reading and understanding ML research papers
• Solid understanding of data preprocessing, augmentation, and annotation best practices for visual data
• Detail-oriented and systematic in evaluating model outputs
• Clear, concise written communicator who can explain technical concepts effectively
• Self-motivated and reliable when working independently

Nice to Have

• MS or PhD in Computer Science, Electrical Engineering, or a related field with a focus on computer vision or machine learning
• Published research in top CV/ML venues (CVPR, ICCV, ECCV, NeurIPS, ICML, etc.)
• Experience with 3D vision, video understanding, generative models, or multimodal AI
• Experience with MLOps tools, experiment tracking, and model evaluation pipelines
• Background in specialized domains such as medical imaging, remote sensing, robotics, or autonomous driving
• Experience with annotation platforms and data quality workflows at scale
• Proficiency in Python and scientific computing libraries (NumPy, OpenCV, scikit-learn)