About this role
Computer Vision Intern — Data Labeling & Annotation
Type: Temporary
Duration: 6 months - 12 months
What You'll Gain
• Exposure to the full CV pipeline, from raw data to deployed model
• Mentorship from CV engineers working on production systems
• Hands-on experience with YOLO, PyTorch, and modern annotation workflows
• Concrete portfolio work — datasets, scripts, and model contributions — that translates directly to future ML/CV roles
What You'll Do
• Annotate images and video for object detection (bounding boxes), segmentation (polygons/masks), and classification
• Help refine labeling schemas and class taxonomies as edge cases come up
• Write Python scripts to convert between annotation formats, validate label integrity, and generate dataset statistics
• QA labels and surface systematic errors or ambiguous cases
• Run baseline YOLO training experiments to evaluate dataset quality and identify labeling gaps
• Document conventions and edge-case decisions
Required
• Pursuing a degree in CS, EE, AI/ML, or related field
• Working knowledge of Python and common CV libraries (NumPy, OpenCV)
• Attention to detail and patience for precision work
Nice to Have
• Hands-on experience with YOLO
• Familiarity with PyTorch, segmentation masks, or model-assisted labeling workflows
Type: Temporary
Duration: 6 months - 12 months
What You'll Gain
• Exposure to the full CV pipeline, from raw data to deployed model
• Mentorship from CV engineers working on production systems
• Hands-on experience with YOLO, PyTorch, and modern annotation workflows
• Concrete portfolio work — datasets, scripts, and model contributions — that translates directly to future ML/CV roles
What You'll Do
• Annotate images and video for object detection (bounding boxes), segmentation (polygons/masks), and classification
• Help refine labeling schemas and class taxonomies as edge cases come up
• Write Python scripts to convert between annotation formats, validate label integrity, and generate dataset statistics
• QA labels and surface systematic errors or ambiguous cases
• Run baseline YOLO training experiments to evaluate dataset quality and identify labeling gaps
• Document conventions and edge-case decisions
Required
• Pursuing a degree in CS, EE, AI/ML, or related field
• Working knowledge of Python and common CV libraries (NumPy, OpenCV)
• Attention to detail and patience for precision work
Nice to Have
• Hands-on experience with YOLO
• Familiarity with PyTorch, segmentation masks, or model-assisted labeling workflows
Tech stack
PyTorchPython
About BrightAI Corporation
BrightAI Corporation is hiring for the computer vision engineer role. NewJob aggregates active openings directly from BrightAI Corporation's applicant tracking system, so this listing is current.
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