HyperVerge Recruitment Drive 2019 – Rs. 45,000 per month

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About Company:-

HyperVerge, Inc. is a Silicon Valley-based deep-learning startup funded by NEA, Milliways Ventures, and Naya Ventures. HyperVerge has developed patent-pending technology for real-time analysis of images and videos obtained from sources such as satellites, surveillance cameras, and documents.

Our models lead the market with top accuracy values on many important data-sets and are optimized for deployments in low bandwidth environments. Deep-learning networks built by HyperVerge are powering applications for large enterprise clients in Financial Services, Defense, Energy, and Security.

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Company Name:-  HyperVerge 

Company Website:-  https://hyperverge.co

Job Position:- Deep Learning Intern

Job Location:- Bengaluru,Karnataka

Salary Package:-

  • Pre-placement offer (PPO) 
  • A stipend of Rs. 45,000 per month

Perks:-

  • Certificate
  • Letter of recommendation
  • Flexible work hours
  • Informal dress code
  • 5 days a week
  • Free snacks & beverages

Eligibility:

  • The preferred choice of branch – Computer Science or Electrical Engineering, students in their final year of college
  • Hands-on experience with Tensorflow and Python
  • Preferred if the student has prior internship experience in Deep Learning
  • Preferred if the student has done Deep Learning courses, perhaps on MOOCs such as Coursera, Udacity, etc.

Other Requirements:-

Only candidates who:

  • are available for full time (in-office) internship
  • can preferably start the internship from January 2020
  • are available for a duration of 6 months (till July’20)
  • have relevant skills and interests
  • have already graduated or will be in the final year of study during the internship period
  •  have prior knowledge and experience with Deep Learning for image/video related problems 

Skill(s) required:–  Python, Deep Learning and C++ Programming

Responsibilities will include, but not limited to:

  1. Using innovative ideas to collect, curate or synthesize data 
  2. Model the problem into a DL framework
  3. Build, measure and iterate on neural network architectures that effectively solve the problem
  4. Benchmark, validate and test the solution for real-world environments
  5. Optimize the solution for accuracy and performance

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