Looking For Internship candidate.About the Opportunity
Join our R&D team as a Computer Vision Intern and help build the perception layer for our innovative inventory automation solution. You will be responsible for developing, optimizing, and deploying computer vision algorithms that extract critical inventory data from high-resolution, field-captured imagery, transforming raw visual input into structured, usable business intelligence.
This is a high-impact role at the intersection of logistics, AI, and systems engineering.
Key Responsibilities
The primary focus is on developing robust, production-ready computer vision modules:
Barcode & QR Code Detection:
Design and implement high-speed detection and decoding algorithms for various 1D and 2D barcodes/QR codes across diverse lighting and angle conditions.
Utilize and benchmark libraries such as
OpenCV
or framework-specific tools (e.g., Google's ML Kit integrations).Optical Character Recognition (OCR):
Develop and fine-tune OCR models to accurately read and verify human-readable text on inventory labels, serial numbers, and pallet placards.
Focus on performance metrics like accuracy and latency for integration into the data pipeline.
Empty Space Identification (Core ML):
Assist in training or refining a custom machine learning model to accurately identify and classify empty pallet locations and open storage spots within the warehouse environment.
Prepare and augment datasets to ensure the model's reliability in identifying empty spaces, a critical metric for optimizing warehouse utilization.
Module Testing and Documentation:
Write comprehensive unit tests for all CV modules.
- Ensure all code is well-documented for seamless integration by the Cloud Backend team.
Required Skills & Qualifications
- Currently pursuing or recently completed a Master's or Bachelor's degree in Computer Science, Machine Learning, or a related field.
- Strong proficiency in Python.
- Hands-on experience with at least one major Computer Vision library (OpenCV
is a must). - Familiarity with deep learning frameworks (
PyTorch, TensorFlow, or Keras
) and common model architectures for object detection (e.g., YOLO, R-CNN). - Experience with image processing techniques and handling large datasets of images.
- Excellent problem-solving skills and attention to detail.
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