Gabriel Parent, Developer in Seattle, United States
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Gabriel Parent

Verified Expert  in Engineering

Bio

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.

Portfolio

GPSE Consulting, LLC
Amazon Web Services (AWS), Scikit-learn, TensorFlow, Python, Machine Learning...
Amazon Music (A2Z)
Amazon Web Services (AWS), Scikit-learn, TensorFlow, Spark, Python...
Amazon.com
Amazon Web Services (AWS), Python, Java, Machine Learning...

Experience

Linux - 16 yearsPython - 12 yearsMachine Learning - 10 yearsNatural Language Processing (NLP) - 10 yearsAmazon Web Services (AWS) - 10 yearsSystem Architecture - 8 yearsSpark - 3 yearsDeep Learning - 3 years

Availability

Part-time

Preferred Environment

Amazon SageMaker, Spark, Jupyter, PyCharm, Git, Linux

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.

Work Experience

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: Amazon Web Services (AWS), Scikit-learn, TensorFlow, Python, Machine Learning, Artificial Intelligence (AI)

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: Amazon Web Services (AWS), Scikit-learn, TensorFlow, Spark, Python, Machine Learning, Artificial Intelligence (AI)

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: Amazon Web Services (AWS), Python, Java, Machine Learning, Artificial Intelligence (AI)

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: Amazon Web Services (AWS), C++, Python, Machine Learning, Artificial Intelligence (AI)

Fixing the Amazon.com Product Catalog

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.
2009 - 2011

Master's Degree in Language Technologies

Carnegie Mellon University - Pittsburgh, Pennsylvania, USA

2005 - 2009

Bachelor of Engineering Degree in Computer Engineering

École Polytechnique de Montréal - Montreal, Quebec, Canada

Libraries/APIs

Scikit-learn, TensorFlow, Pandas, NumPy

Tools

Amazon Elastic MapReduce (EMR), Amazon Elastic Container Service (ECS), Amazon SageMaker, Git, PyCharm, Jupyter

Languages

Python, Java, Bash Script, SQL, JavaScript, C++, HTML

Platforms

Android, Linux, Amazon Web Services (AWS), Amazon EC2, Docker

Storage

Amazon S3 (AWS S3)

Frameworks

Django, Spark

Paradigms

Scrum

Other

Machine Learning, Data Science, Natural Language Processing (NLP), Data Mining, Information Extraction, System Architecture, Big Data, Generative Pre-trained Transformers (GPT), Artificial Intelligence (AI), Deep Learning, Distributed Systems, Recommendation Systems, Cython

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