Data Engineer

Company Details

Kawa Space focuses on Developer APIs, App Stores, Connected Devices, Earth Observation, and Space Satellites. Their company has offices in San Francisco and Mumbai. They have a small team that's between 1-10 employees.

Kawa Space is building a software-driven geo-intelligence platform. Kawa employees enjoy the opportunity and benefits of working at an early-stage space startup, but in an environment much more akin to a later stage company given the experience of the founding team. The culture at Kawa encourages innovation, independent problem solving, and collaboration as we continue to mature our product in the ever-changing world of space intelligence.

We have

  • Employer-paid health care
  • Remote and distributed team
  • Casual and diverse workplace
  • Unlimited Vacation policy
  • Stock options


  • Energized to join a startup
  • Excited to mentor more junior developers
  • Good at problem selection, problem solving, and course correcting
  • Focused on best practices
  • Highly pragmatic and collaborative

You will

  • Design and implement highly scalable computer vision and machine learning algorithms for use on geo-referenced or geo-spatial data
  • Collaborate with end-user product owners to understand computer vision and machine learning needs and set up the image processing and data analytics software pipeline enabling kawa to serve customer specific data products
  • Rapidly iterate and improve algorithms to continuously improve precision, recall and other suitable metrics under a variety of challenging conditions
  • Collaborate closely with peers in other engineering teams and interface frequently with product teams

You have

  • Bachelor’s Degree in computer science or other equivalent experience
  • Experience with automated analysis of geo-referenced or geo-spatial data
  • Experience in handling GIS data
  • 4+ years experience in computer vision, machine learning, and/or image processing
  • Strong Python software development skills
  • Expertise in deep learning, including semantic segmentation, object detection, and image classification
  • Experience with deep learning frameworks such as Tensorflow
  • Experience with cloud computing (AWS, Google Cloud or similar)


Tagged as: machine learning, geo-spatial data, statistical data analysis, linear models, multivariate analysis, stochastic models, sampling methods, large datasets

Select your currency
EUR Euro
AUDAustralian dollar
Visit us on LinkedInVisit us on FacebookVisit us on Twitter