Computer Vision & ML Expert

About the Role

Alignerr is seeking Computer Vision & Machine Learning Experts for an hourly contract role, based in Bangalore and across India. This is a fully remote, flexible position focused on evaluating, improving, and guiding AI models that process visual data.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

Key Responsibilities

• 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.

Requirements & Tech Stack

• 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.

Qualifications (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).