Pear VC

Pear VC

Founding Applied AI Product Engineer

New York City · Exec

Sponsorship not specifiedDetected 182 days ago
JavaScriptTypeScriptReactLLMsFigma

About the role

  • We're not another test tool - we're reimagining how digital teams ship with confidence.
  • Top brands (Alo Yoga, Living Spaces, HelloFresh, Nextdoor, Abercrombie & Fitch, and more) are actively pulling us in.
  • We believe in delighting our users - how can we make this feature as delightful and magical for the end-user? - Are curious.

Responsibilities

  • Ship end-to-end features -- design, code, test, deploy, and own them in production. You'll own parts of the product end-to-end
  • Build and operate both performance frontend and backend code
  • Rapidly iterate with product, engineering, design, and founders
  • Have build LLM products in the past
  • Have high autonomy and drive projects on your own - you don't wait to be told what to do

Requirements

  • Have very strong technical experience with Typescript / Next-JS / React front-end frameworks.

Benefits

  • High quality medical, dental, and vision coverage options, flexible depending on your needs.
  • Meaningful equity in a fast-growing company
  • You will be dealing with a lot of new technology and challenges every day as we scale-you should be comfortable learning new frameworks on the fly, and in communicating this knowledge to the team.
  • Are curious and comfortable learning new frameworks and technologies on the fly

Company info

  • We want to help our customers deliver the best digital experiences
  • Read our Manifesto: https://www.spurtest.com/founders-letter
  • Launching new features, speaking directly with customers to gather feedback, iterating quickly.

Visa & Work Authorization

  • n, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by app

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