Daniel Nouri, Machine Learning Developer in Berlin, Germany
Daniel Nouri

Machine Learning Developer in Berlin, Germany

Member since December 4, 2018
Daniel is a machine learning specialist focusing on deep learning, a software engineer with over 18 years of experience in building reliable, high-performing systems, and the owner of Natural Vision UG, based in Berlin, Germany. An exceptional communicator and self-starter who's contributed to many projects over the years (including Scikit-learn), Daniel joined Toptal to find work that piques his interest.
Daniel is now available for hire




Berlin, Germany



Preferred Environment

Emacs, Git, Linux

The most amazing...

...thing I've done was when I led a team of researchers/engineers to improve on a state-of-the-art app with real-time medical image analysis.


  • Machine Learning Lead | Software Engineer | Trainer

    2014 - PRESENT
    Natural Vision UG
    • Developed and deployed a predictive analytics system for parcel delivery for a Fortune Global 500 company.
    • Developed a predictive analytics system for sales forecasts which outperforms human analysts in terms of speed and accuracy for a Fortune Global 2000 company.
    • Developed a deep-learning-based bioacoustics recognition system for detection and classification of marine mammals, in collaboration with Oregon State University.
    • Helped Jetpac, a company that built city guides using artificial intelligence, implement a deep-learning-based computer vision system that finds objects inside millions of Instagram photos. The extracted info was then used to automatically categorize.
    • Helped build up an in-house corporate data science team at a Fortune Global 500, teaching software engineering best practices and guiding a research-oriented team towards developing robust, maintainable, and production-ready software.
    Technologies: Scikit-learn, Python, Deep Learning, Docker, Deployment, CI/CD Pipelines, Data Science, Computer Vision, Image Recognition
  • Principal Software Engineer

    2020 - 2020
    Cloud Governance Startup
    • Worked on building an open-source project to enable testing of cloud policy configuration rules written in Terraform and CloudFormation before deployment.
    • Worked on several bugfixes and improvements on the company's open-source software.
    • Developed onboarding documentation for a rapidly growing team.
    Technologies: Python, Amazon Web Services (AWS), Azure, Google Cloud Platform (GCP), Terraform, AWS CloudFormation
  • Lecturer

    2014 - 2018
    Data Science Retreat
    • Regularly gave courses around Python, Scientific Python, machine learning, and deep learning.
    • Developed course material and hands-on tutorials.
    • Advised students on portfolio projects and career choices.
    Technologies: Machine Learning, Python
  • Chief Scientist

    2016 - 2017
    Samsung Research America (Samsung NEXT)
    • Researched and developed algorithms for the estimation of liquid flow inside of water pipes, using an IoT sensor that is attached to the outside of the pipe.
    • Implemented signal processing and machine learning algorithms for lower-power, embedded devices.
    • Built a rig for the semi-automatic acquisition of labeled training data for water flow detection; using Raspberry Pi, an inline flow meter for ground truth, and several sensors.
    • Developed low-power, digital wireless communication protocols. Built a system for remote maintenance of sensors and communication hubs.
    Technologies: Internet of Things (IoT), C, C++, Machine Learning, Microcontrollers, Python
  • Head of Machine Learning

    2015 - 2016
    Butterfly Network
    • Led a team of data scientists in solving problems in medical image analysis.
    • Implemented real-time, state-of-the-art computer vision applications for ultrasounds.
    • Managed the acquisition of medical imaging data and worked with medical experts to create rich datasets.
    • Researched and developed novel applications for ultrasound and MRI.
    Technologies: Medical Imaging, Python, Convolutional Neural Networks, Amazon Web Services (AWS), NumPy



  • Languages

    Python, C, C++, SQL, JavaScript
  • Libraries/APIs

    PyTorch, Scikit-learn, TensorFlow, Pandas, NumPy, OpenCV
  • Tools

    Git, Emacs, Pytest, Terraform, AWS CloudFormation
  • Paradigms

    Automated Testing, Pair Programming, Agile, Data Science
  • Platforms

    Docker, Linux, Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP)
  • Other

    Artificial Intelligence (AI), Deep Learning, Image Recognition, Predictive Analytics, Convolutional Neural Networks, Medical Imaging, CTO, Debugging, Machine Learning, Computer Vision, Natural Language Processing (NLP), Computer Vision Algorithms, Time Series Analysis, Sensor Data, Hardware, Signal Processing, Full-stack, Microcontrollers, Internet of Things (IoT), Deployment, CI/CD Pipelines
  • Storage

  • Frameworks



  • Neural Networks for Machine Learning
    University of Toronto via Coursera
  • Machine Learning
    Stanford University via Coursera

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