Hire remote machine learning engineers near Portland or in a similar time zone
Gabriel Parent
Freelance Machine Learning Engineer
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.
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David Dai
Freelance Machine Learning Engineer
David has extensive experience building machine learning and deep learning (DL) capable solutions in top companies (such as Google, Facebook, Apple, LinkedIn), startups (Petuum), and academia (earning a Ph.D. from Carnegie Mellon University, with publications in top venues and 1,700 citations). He holds multiple patents in DL-based medical imaging tech and large-scale AI systems and has grown an AI team to 60+ as the director and tech lead.
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Robby Goetschalckx
Freelance Machine Learning Engineer
Robby is a machine learning expert with 10+ years of experience in research and back-end software development for machine learning solutions. With master's degrees in computer science and artificial intelligence in addition to his Ph.D. in computer science, Robby is well equipped to provide solutions to a variety of issues in companies of all sizes.
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Zach Boldyga
Freelance Machine Learning Engineer
Zach’s abilities are best captured through his recent, six-year founding and management of Scalabull, a software company that currently powers over 1 million patient lab testing transactions in the US annually. Zach has also helped build open-source software since 2008, with recent projects including Tensorflow, Apache MXNet, and React.js components.
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Blake Byerly
Freelance Machine Learning Engineer
Blake possesses both startup and large enterprise experience leveraging machine learning (deep learning and classical techniques) to create value. He has applied machine learning to a variety of problems including network event correlation and incident forecasting, resource-constrained scheduling, and at-scale E-commerce applications.
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Eric Hare
Freelance Machine Learning Engineer
Eric is the chief data scientist at the Omni Analytics Group. He has also graduated with a Ph.D. degree in statistics and computer science from Iowa State University. He is the most proficient working in R, RStudio, and associated R packages—including Shiny, R Markdown, and ggplot2.
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Ryan Zotti
Freelance Machine Learning Engineer
A data scientist and data engineer, Ryan can take projects from concept to deployment. He is a machine learning expert with a knack for impactful data visualizations. Ryan has collaborated with innovators in diverse fields, presented at tech conferences, and co-authored groundbreaking research papers in respected journals such as the Lancet. He is also the author of a popular autonomous vehicle project on GitHub.
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Neal Barnett
Freelance Machine Learning Engineer
Neal is a senior consultant and database expert who brings a wealth of knowledge and over two decades of experience to the table. He is well versed in security, performance optimization, upgrades, platform configuration, and schema changes. He has touched every aspect of database handling, from architecture and hands-on development to project management.
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Jack Kwok
Freelance Machine Learning Engineer
Jack has over a dozen years of experience developing complex software systems. He is experienced in applying deep learning techniques to solve Computer Vision problems such as object detection, multi-object tracking, and ND semantic segmentation, to name a few. Jack has worked on state-of-the-art Computer Vision models to solve autonomous driving problems. He is also fluent in traditional ML techniques and has an expert rating at Kaggle.
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Neal Cheng
Freelance Machine Learning Engineer
Neal has a professional track record of success over the past decade working with a variety of clients. For example, he's improved monthly item sales for 10% to 40% by implementing a machine learning model to predict customer demand. He's looking forward to helping more clients achieve their goals through the use of data science and technology.
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