Mobile Algorithm Deployment Intern

1 Month ago • All levels

Job Summary

Job Description

This internship focuses on computer vision SDK development and algorithm deployment on edge NPUs. Responsibilities include optimizing performance, following model quantization and optimization processes, and modifying models based on hardware optimization. The role also involves designing model evaluation systems for business scenarios and improving accuracy through data augmentation and loss function optimization. Candidates should have strong C++/Python skills and be familiar with model compression algorithms.
Must have:
  • Bachelor's degree or above
  • Strong C++/Python development and algorithm engineering capabilities
  • Paper reading and reproduction capabilities
  • Familiarity with quantization and other model compression algorithms
  • Good communication and collaboration skills
  • Passion for technical challenges and business implementation
  • Internship for more than 4 days a week, lasting more than 6 months
Good to have:
  • Mobile application development or Unity experience

Job Details

Job Description

1. Responsible for SDK development in the direction of computer vision and algorithm deployment and performance optimization on the edge NPU;

2. Follow up on model quantization and optimization processes, and modify models based on hardware optimization;

3. Design a model evaluation system based on business scenarios, and improve accuracy through data augmentation, loss function optimization, and other means.

Job Requirements

1. Bachelor's degree or above, strong C++/Python development and algorithm engineering capabilities, as well as paper reading and reproduction capabilities;

2. Familiar with model compression algorithms such as quantization, possess good communication and collaboration skills, and are passionate about technical challenges and business implementation;

3. Mobile application development or Unity experience is preferred.

4. Intern for more than 4 days a week, lasting more than 6 months, and those who can start as soon as possible are preferred.

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