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Computer Vision ML Engineer at Passio Inc. (allows remote)


Passionate about Artificial Intelligence, huge-scale data challenges, and applications of AI in nutrition, healthcare and human performance?

Read On. This could be your opportunity!

Passio is looking for a machine learning engineer to join our team in Menlo Park CA, Munich Germany or working remotely. 

We created neural networks that run on-device, exhibit real-time performance and enable applications that were not feasible with the earlier technology. Take a look at how our edge-ai is applied in the field of food recognition.

We are looking for talented software engineers to help us take this technology to the next level.


  • Develop next gen computer vision networks¬†for classification and segmentation
  • Implement large-scale computer vision training pipelines
  • Implement on-device training and federated learning systems
  • Develop visual-semantic embeddings for learning joint spaces of visual & textual concepts
  • Explore ways to speed up the¬†performance of neural networks on edge devices.


  • 2+ years of hands-on experience with computer vision neural networks
  • Experience building computer vision training piplelines
  • Experience with classification, segmentation and visual-semantic embeddings
  • Experience with TensorFlow and training on cloud-based GPUs
  • Experience deploying/running ML codes in the cloud and on the edge
  • Agility and willingness to work in a rapidly growing startup environment

Requirements (Applicants not meeting these requirements will be discarded):

  • Please share your GitHub or code portfolio
  • Please describe (in 1-2 bullet points) your experience with computer vision projects

Why apply:

  • You want to excel in a rapidly growing and highly demanding startup environment
  • You pride yourself as a visionary engineer, researcher and developer and your ambition is to change the lives of millions of people
  • You are humble and intellectually curious
  • You can see the opportunity and are courageous enough to take it

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