Jack Kwok, Deep Learning Developer in San Francisco, CA, United States
Jack Kwok

Deep Learning Developer in San Francisco, CA, United States

Member since May 23, 2019
Jack has 20 years of experience developing complex software systems. He is experienced in applying deep learning techniques to solve Computer Vision problems, including object detection, multi-object tracking, instance segmentation, etc. Jack has worked on state-of-the-art Computer Vision models to solve healthcare and autonomous driving problems. He is also skilled in natural language processing and traditional ML techniques and has an expert rating at Kaggle.
Jack is now available for hire


  • Lyft
    Amazon Web Services (AWS), Object Detection, Deep Learning, AWS, OpenCV...
  • Insight Data Science
    Keras, Semantics, Deep Learning
  • LinkedIn
    Devices, Mobile, Video Codecs, Java



San Francisco, CA, United States



Preferred Environment

Amazon SageMaker, Google Cloud ML, Amazon Web Services (AWS), Scikit-learn, AWS, Windows, Linux, PyTorch, Python

The most amazing...

...project I have worked on involved building a neural network from scratch to recognize handwritten digits. It led me to pursue a career in deep learning.


  • Staff Software Engineer | Computer Vision and Machine Learning

    2017 - 2020
    • Applied deep learning and computer vision algorithms to solve autonomous driving problems.
    • Led the design and development of custom deep learning-based multi-object trackers used by multiple teams to track cars, cyclists, pedestrians, and traffic control elements.
    • Designed the initial production release of the active learning data pipeline for continuous machine learning model improvement.
    Technologies: Amazon Web Services (AWS), Object Detection, Deep Learning, AWS, OpenCV, Python, TensorFlow, PyTorch
  • Artificial Intelligence Fellow

    2017 - 2017
    Insight Data Science
    • Developed a Computer Vision system based on a convolutional neural network that automatically annotates post-hurricane flooded roads on satellite imagery.
    • Developed an open-source project on GitHub.
    • Researched and published a blog post about the use of deep learning for disaster recovery.
    Technologies: Keras, Semantics, Deep Learning
  • Staff Software Engineer

    2015 - 2017
    • Helped build LinkedIn's video platform from scratch.
    • Wrote a technical blog post on building a Native video player library for Android.
    Technologies: Devices, Mobile, Video Codecs, Java
  • Software Architect

    2011 - 2015
    • Scaled our mobile platform to over a dozen apps across iOS, and Android. This grew the audience over ten times. Trulia mobile apps are top-rated by users, and frequently selected for prestigious editorial features by Apple, Google Android (Editors' Choice, and Staff Pick), Amazon, and Samsung.
    • Developed and built projects for iOS and Android involving geofencing and geospatial data, user data synchronization, cross-platform communication, and natural language processing.
    Technologies: REST APIs, iOS, Android
  • Technical Leader

    2006 - 2011
    • Led the architectural designs of Cisco Jabber (an enterprise VoIP app) on Android. Worked with product managers to translate business needs into technical specifications. Worked with cross-functional teams to define requirements, interfaces, and implementation approaches, and led a group of six talented Android developers to build out layered components of the application from scratch.
    • Extended the Blackberry client with innovative voice features, coordinated development activities, and mentored junior engineers.
    Technologies: CODE, VoIP, Android
  • Senior Software Engineer

    2004 - 2006
    Weathernews Americas
    • Developed the prototype, architecture, design, and implementation of Weathernews LiveLocal. LiveLocal is the US’s first streaming video application bringing videos from local broadcast TV stations to cell phones.
    Technologies: Videos, Mobile


  • Deep Learning for Disaster Recovery

    I attempted to automatically annotate flooded, washed out, or otherwise severely damaged roads using state-of-the-art deep learning methods. My goal was to create a tool to help detect and visualize anomalous roads in a simple user interface.

    For details, see the blog post:


  • Libraries/APIs

    PyTorch, OpenCV, Scikit-learn, NumPy, Keras, Pandas, SciPy, REST APIs, TensorFlow
  • Tools

    Scikit-image, Amazon SageMaker
  • Other

    Computer Vision Algorithms, Deep Learning, Computer Vision, Object Detection, Object Tracking, Image Processing, Machine Learning, Mobile Apps, Linear Algebra, Convolutional Neural Networks, AWS, Devices, Videos, Google Cloud ML, Video Codecs, Recurrent Neural Networks, Natural Language Processing (NLP)
  • Languages

    Python, Java
  • Platforms

    Google Cloud Platform (GCP), Linux, Windows, Mobile, Android, iOS, Amazon Web Services (AWS), Kubernetes


  • Master of Engineering Degree in Computer Science
    2001 - 2002
    Massachusetts Institute of Technology - Cambridge, MA
  • Bachelor of Science Degree in Computer Science
    1997 - 2001
    Massachusetts Institute of Technology - Cambridge, MA
  • Bachelor of Science Degree in Mathematics
    1997 - 2001
    Massachusetts Institute of Technology - Cambridge, MA


  • Google Cloud Platform Professional Machine Learning Engineer
    MARCH 2021 - PRESENT

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