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Machine Learning / Deep Learning Engineer

As Machine Learning Engineer you will contribute to building a machine learning platform to provide users with a personalised experience, and optimise the research back-end of the platform. Joining us you will be part of a small and tight team that will develop our product from scratch.

WHO WE THINK YOU ARE

You have several years experience as a backend developer or as a data scientist. You have a strong interest for AI, and have recent experience working with machine learning frameworks and data science methodologies.

You are passionate about learning, and implementing complex systems to solve real-world challenges. Implementing complex data structures or optimizing distributed machine learning deployments is something that you're excited about.

You have probably used frameworks such as Spark, Scala, Akka, Cassandra, Accumulo, Hbase, Hadoop, HDFS, AVRO, MongoDB, or Mesos.

It would be awesome if you have a robust portfolio on Github and / or open source contributions you are proud to share.

You have experience working in agile teams, and are excited about working closely with a tight team consisting of product, design, and domain experts.

Since our mission is to make life better for others, we do value a passion for personal development. 29k will surround you with great minds that will allow you to transform your technical skills into something magical.

THE 29K WAY

  • We are passionate about personal development & inner growth
  • We are courageous enough to bring our whole self to work
  • We value honesty, respect and willingness over perfection
  • We strive to integrate all parts of ourself in the decision making process: rational and emotional
  • We feed on our differences, and enjoy co-creating with colleagues of different expertise
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Norrsken House, Stockholm

Birger Jarlsgatan 57
113 56 Stockholm Directions

Norrsken House & Klustret

29k is backed by people who believe technology can be used to transform personal growth.

It is co-funded by Norrsken Foundation and Ekskäret Foundation, and supported by researchers from Harvard University and Karolinska Institutet.

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