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Graze.ai

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About

Graze.ai is a data scraping and web data extraction tool that uses machine learning to find hard-to-find and deleted data. It crawls 50-60 million sites a week and combines this data with its contributor network. Graze.ai's machine learning approach uses transfer learning on a Large Language Model (LLM) architecture and few-shot learning (FSL) with custom-sourced databases to understand subtle nuances and provide accurate data. The tool is used by businesses for various applications including competitor analysis and market research. The company also indicates a 0% customer acquisition cost and revenue increase for its clients.

Platform
Web
Task
data scraping

Features

competitor analysis

data classification

market research

data scraping

few-shot learning (fsl)

large language model (llm) architecture

custom-sourced databases

web data extraction

Job Opportunities

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Graze.ai

Machine Learning Engineer

Graze.ai is an AI-powered data scraping tool that provides hard-to-find business intelligence by crawling millions of websites and combining it with a contributor network.

engineeringremotefull-time

Benefits:

  • Distributed and fully remote culture

  • Remote work budget for your setup and laptop

  • Unlimited vacation with 3-weeks required vacation

  • 99% coverage for Health Care, Dental, and Vision

  • Unlimited mental or physical health leave

Education Requirements:

  • PhD or equivalent Master's Degree plus 1+ years of experience in CS, CE, ML or related field

Experience Requirements:

  • 1+ years of experience of building machine learning models for business application

Other Requirements:

  • Experience programming in Python

Responsibilities:

  • Participate in the design, development, evaluation, deployment and updating of data-driven models in machine learning

  • Develop and/or apply statistical modelling techniques (e.g. Bayesian models, deep neural networks), optimization methods, and other ML techniques to different applications in scraping

  • Play a key role in developing tools and abstractions that our other developers would build on top of

  • Leverage billions of data points and large-scale computing resources to accelerate advances in language classification

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