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Internship

Intern, Edge Compute

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Planet

•

1mo ago

🚀 Off-cycle Internship

San Francisco

⌛ Closed
Applications are closed

Off-cycle Internship

Data•San Francisco

Description

  • Join Planet's Edge Compute Team as an Edge Compute Spacecraft Intern! We conduct tech demos and operational constellations and we're designing, manufacturing and launching record-breaking numbers of spacecraft. As an intern and under the mentorship of experienced professionals, you will be an integral part of our design process, prototyping and testing edge compute modules and algorithms for processing data onboard satellites. 
  • This is a fantastic opportunity to learn, grow, and engage with seasoned Planeteers from teams such as the analytics machine learning team and work to co-develop edge computing solutions for Planet’s next generation of spacecraft.

Requirements

  • Experience with computer vision, machine learning, and deep learning-based approaches
  • Proven ability deploying algorithms and solutions to solve real-world problems in computationally constrained environments
  • Proficiency in C++, Python, or related tools and system SW architecture
  • MS or PhD in EE/CS/Physics
  • What Makes You Stand Out:
  • Experience managing and doing GPU-based computing on large data sets
  • Experience with machine learning on earth observation data sets
  • EAR/ITAR Requirements:
  • This position requires access to export-controlled technology, and as such, and in accordance to applicable law, employment is only open to US citizens, US nationals, lawful permanent residents of the US or persons admitted to the US as an asylee or refugee.

Education requirements

Currently Studying
Masters
PhD

Area of Responsibilities

Data

Responsibilities

  • Impact You’ll Own:
  • Hardware/software co-development of edge computing solutions for Planet’s next generation spacecraft
  • Develop pre-processing of raw imagery onboard the satellite to make them useable by the machine learning algorithms
  • Collaborate with the analytics machine learning team to implement deep-learning-based classification systems on pre-processed imagery for identifying, characterizing, and classifying objects
  • Explore and develop new methods for improving detection performance and improving the runtime of inference

Details

Work type

Full time

Work mode

office

Location

San Francisco