Key Skills

Job Description

Computer Vision & ML Expert (AI Training) 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 in the Amsterdam area and across the Netherlands to evaluate, improve, and guide AI models that process images, video, and visual data — helping build systems that truly see. The Netherlands hosts a thriving ML research community — from the University of Amsterdam's leading computer vision group to innovative industry labs. 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. Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week What You'll Do Evaluate AI-generated outputs related to image recognition, object detection, segmentation, and visual reasoning tasks Assess the quality, accuracy, and robustness of computer vision model predictions and outputs Identify failure modes, edge cases, and biases in visual AI systems Create, review, and refine training data annotations for computer vision pipelines Provide expert-level feedback on model behavior, architecture decisions, and output quality Write detailed technical evaluations and suggest concrete improvements Work across diverse visual domains — from natural images and medical imaging to autonomous systems and document understanding Complete task-based assignments independently on your own schedule 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 Familiarity 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) Why Join Us Work on cutting-edge AI projects alongside leading research labs Fully remote and flexible — work when and where it suits you Freelance autonomy with the structure of meaningful, task-based work Make a direct, tangible impact on how AI systems perceive and interpret the visual world Engage with diverse, challenging problems across multiple visual domains Potential for ongoing work and contract extension as new projects launch Join a global community of top-tier AI practitioners shaping the future of machine intelligence

Core Responsibilities

Evaluate AI-generated visual outputs and assess the accuracy, robustness, and quality of computer vision models across tasks and domains. Identify failure modes and biases, refine training annotations, and provide detailed technical feedback and improvement recommendations.

Requirements

Candidates should have strong computer vision fundamentals, hands-on experience with deep learning frameworks, and familiarity with common vision architectures and research papers. They should understand visual data preprocessing and annotation practices, communicate technical evaluations clearly, and work reliably and independently.

Benefits

• Flexible Remote Work
• Flexible Schedule
• Potential for Ongoing Work and Contract Extension
• Work on Cutting-Edge AI Projects
• Meaningful, Task-Based Work