Greptile

Greptile

Enterprise Account Executive

San Francisco · Full-time

Sponsorship not specifiedDetected 190 days ago
Code ReviewMachine LearningMLOpsFigmaSalesAccount ManagementOutbound Sales

About the role

  • Greptile is an AI code reviewer that catches bugs and anti-patterns in pull requests with complete context of the codebase.
  • Hundreds of top software companies use Greptile to merge PRs faster and catch more bugs.
  • Greptile reviews over 1B lines of code every month.

Responsibilities

  • Own the full enterprise sales cycle from prospecting to close across technical and economic buyers
  • Identify, prioritize, and develop strategic relationships with top software organizations
  • Develop deep product knowledge and demonstrate strong technical fluency in the AI/code review space
  • Partner closely with founders, engineers, and product to feed customer insights into product direction

Requirements

  • 5+ years of closing experience in B2B SaaS, with at least 2+ in enterprise sales
  • Strong technical fluency-you've sold to engineers, and you can comfortably explain APIs, code integrations, or model behaviors
  • A proven track record of exceeding quota in complex sales cycles
  • Comfortable in ambiguity-you know how to build structure in a fast-moving, zero-to-one environment
  • Entrepreneurial spirit and first principles thinking
  • This role is expected to be in person full-time for the first eight weeks, and thereafter will follow a hybrid schedule of at least three days per week in office.

Nice to have

  • Background selling into developer tools, MLOps, security, or dev productivity
  • Experience with bottoms-up adoption funnels
  • Previous founder, early operator, or first sales hire
  • Prior experience at an early-stage AI company (Series A to C preferred)

Skills

  • Raised $30M from Benchmark, YC, Paul Graham, Initialized
  • Scaled from $0 to 8 figures in ARR with just two people on our GTM team

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