Gabriel Parent, Natural Language Processing (NLP) Developer in Seattle, WA, United States
Gabriel Parent

Natural Language Processing (NLP) Developer in Seattle, WA, United States

Member since April 15, 2019
Gabriel completed his master's before spending six years at Amazon working with the Search and Discovery team, and the Music organization. He has nine years of experience building distributed systems with AWS, including natural language processing, speech processing, recommendation systems, and deep learning. Gabriel is a team player who onboarded and mentored several new hires, and junior engineers at Amazon.
Gabriel is now available for hire

Portfolio

Experience

  • Linux, 16 years
  • Python, 12 years
  • Amazon Web Services (AWS), 10 years
  • Natural Language Processing (NLP), 10 years
  • System Architecture, 8 years
  • Machine Learning, 8 years
  • Spark, 3 years
  • Deep Learning, 3 years

Location

Seattle, WA, United States

Availability

Part-time

Preferred Environment

Linux, Git, PyCharm, JupyterLab, SageMaker, Spark

The most amazing...

...algorithm I've developed helped millions of Amazon.com customers find the right products out of a catalog of billions of items.

Employment

  • Senior Machine Learning Engineer

    2018 - PRESENT
    GPSE Consulting, LLC
    • Created a five-day machine learning engineering workshop to help companies bootstrap machine learning teams internally.
    • Customized an online lab platform based on Jupyter to host workshops, including one-touch sign-in with YubiKeys.
    • Provided consulting services to companies looking for machine learning expertise.
    Technologies: AWS, Python, TensorFlow, Sklearn
  • Machine Learning Engineer II

    2015 - 2017
    Amazon Music (A2Z)
    • Developed a generative model for automated playlist creation.
    • Designed a multi-modal approach to music classification to improve customer experience across several applications.
    • Fixed genre misclassification issues for over 100,000 tracks.
    • Acted as a machine learning consultant for several external teams.
    Technologies: AWS, Python, Spark, TensorFlow, Sklearn
  • Software Development Engineer II

    2011 - 2015
    Amazon.com
    • Developed, and maintained a faceted search system with an attributed yearly sale of over $500 million.
    • Developed, and deployed a new algorithm for faceted search across several categories and countries.
    • Delivered over 10 tech talks on various topics such as AWS, machine learning, and crowdsourcing.
    • Mentored five engineers, and interviewed over 130 candidates.
    • Organized a Hackday for three consecutive years for an organization of over 300 engineers.
    Technologies: AWS, Java, Python
  • Research Assistant

    2009 - 2011
    Carnegie Mellon University
    • Maintained the Let's Go Bus information system.
    • Transcribed over one million speech utterances with crowdsourcing.
    • Organized monthly lunches on crowdsourcing for language technologies.
    Technologies: AWS, Python, C++

Experience

  • Fixing the Amazon.com Product Catalog (Development)
    https://patents.google.com/patent/US20150378975A1/en

    One of my key contributions in my first role at Amazon was tackling the missing data problem: Not all attributes (e.g., hard drive interface, and capacity) are specified by merchants, although that information is often provided in the title or description.

    I designed and coded several components of the larger pipeline my team was powering. More importantly, I developed a new algorithm for phrase extraction, and scaled it to handle thousands of categories, and billions of products across several marketplaces. EMR (Hadoop, Spark), S3, and several other AWS offerings were integral to the overall architecture, and are now essential elements of my design toolkit.

    Knowing that my work had a significant positive impact on several downstream systems (recommendation, search, browse) was a great motivation, but the technical challenge was a driver too. Reprocessing a very large collection of text documents daily, and cost-effectively is not a trivial task. Applying machine-learning at that scale also comes with fun challenges.

Skills

  • Languages

    Python, Java, Bash Script, SQL, JavaScript, Solidity, C++, HTML
  • Frameworks

    AWS EMR, Django, Spark
  • Libraries/APIs

    Sklearn, TensorFlow, Pandas, NumPy
  • Paradigms

    Data Science, Scrum
  • Platforms

    Linux, Amazon Web Services (AWS), AWS EC2, Blockchain, Docker
  • Storage

    AWS S3
  • Other

    Machine Learning, Natural Language Processing (NLP), Data Mining, Information Extraction, System Architecture, Big Data, Deep Learning, Distributed Systems, Recommendation Systems, Cython
  • Tools

    AWS ECS, Amazon SageMaker

Education

  • Master's degree in Language Technologies
    2009 - 2011
    Carnegie Mellon University - Pittsburgh, Pennsylvania, USA
  • Bachelor of Engineering degree in Computer Engineering
    2005 - 2009
    École Polytechnique de Montréal - Montreal, Quebec, Canada

To view more profiles

Join Toptal
I really like this profile
Share it with others