Extend

Extend

Machine Learning Researcher

New York City

Sponsorship not specifiedDetected 366 days ago
Machine LearningLLMsResearchLeadership

About the role

  • Extend is building a modern document processing cloud. We're on a mission to transform how the world works with unstructured data.
  • As a Machine Learning Engineer at Extend, you'll be responsible for building state-of-the-art document processing infrastructure. The work you do will impact every single customer across the millions of documents that our system ingests and processes every month.

Responsibilities

  • We take the time to understand your needs to craft a package designed for you:
  • The market for document processing has expanded 1000x due to LLMs, and all existing solutions are low NPS
  • Design novel LLM techniques for increasing the complexity of use cases and data streams that Extend can be applied to
  • Have complete ownership over the work you do - as a founding team member, you'll have the opportunity to truly own large areas of product and engineering and have direct relationship with customers.
  • Work directly with the CEO, CTO, and other founding members and help build out our team.

Benefits

  • Insurance - 90% health insurance premium coverage
  • Unlimited PTO policy - we trust everyone like owners, take what you need
  • Learning and development investment - we invest heavily in our team and support your growth
  • You'll be joining a talent dense team (e.g. former founders, world record holders) operating in a high performance culture, in-person in NYC, with high equity ownership
  • Training and deploying SOTA vision models for document processing

Equal opportunity

  • Equal Opportunity Employer.

Visa & Work Authorization

  • eligion, sex, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, citizenship status, marital status, pregnancy, protected veteran status, or any other characteristic

This listing is sourced directly from Extend's careers page and normalized into a canonical job model.