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
Task
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
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.
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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