Applied Scientist – Machine Learning Engineer

SpaceKnow provides transparency to global changes and trends by combining the world’s largest collection of satellite imagery with a proprietary artificial intelligence engine. Our vision is to index the physical world and empower users with near-realtime large-scale analysis to drive decision making.

SpaceKnow has two distinct products: a web-based, on-demand geospatial analytics platform (also available through API), as well as a growing suite of economic index offerings.

Join a team of passionate Machine Learning Engineers to advance the state of the art in novel machine learning approaches and data science tools for processing satellite images!

You should have a proven track record of creatively solving difficult problems and you should enjoy spending your time writing code and producing results. Have experience with big data analysis, image processing, deep neural networks and cloud computation is a great plus.


  • Software development experience in data manipulation, data mining or prototype development using Python (agile development, software version control, continuous integration, test driven development, pair programming, rapid prototyping)
  • Data analysis and signal processing skills with an ability to independently prototype machine learning (deep learning) pipelines
  • Understanding of mathematical modeling, computer vision, statistical analysis, data mining
  • Skilled in creative, strategic thinking and an ability to propose and deliver simple solutions to complex puzzles
  • Excellent communication and collaboration skills
  • Self-motivated and independent
  • Masters or PhD in computer science, machine learning, data science or signal processing


  • Free drinks and food
  • Work-life balance
  • Vacation & Paid Time Off
  • Teambuildings on regular bases

Joining Spaceknow, you will join a young team of talented and highly motivated people who strive to make an impact on the world but also have fun along the way.

Do not hesitate to send links of your contributions to FOSS projects or of any other relevant work.

Interested? Apply at

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