Staff Software Engineer, Gtm Systems

Anthropic Anthropic · AI Frontier · San Francisco, CA · Engineering & Design - Product

Staff Software Engineer for GTM Systems at Anthropic, focusing on building and maturing the engineering pipeline and agentic tooling for production systems. This role involves leading architecture reviews, establishing AI-assisted development standards, and collaborating with other teams to build shared platforms.

What you'd actually do

  1. Lead architecture reviews for GTM systems that process lead-to-cash transactions and generate the data points the organization runs on, including changes originating in other teams that land on our platform
  2. Architect and mature the engineering pipeline the team develops against: pull request workflow, CI/CD, automated UAT, and the deploy path from source control to production
  3. Establish shared AI-assisted development standards for the team, converting existing individual usage into consistent practice that holds up under review
  4. Work with engineering teams outside GTM on shared architectural problems and common tooling, where the right answer is a platform decision rather than a local one
  5. Build and shape the agentic tooling running on top of our platform, and set the pattern other engineers follow when they build the next one

Skills

Required

  • strong software engineering fundamentals: version control discipline, testing, code review, deployment automation, and a bias toward simple designs
  • built the delivery pipeline for a team
  • led a team through a step change in engineering practice
  • Work fluently with AI coding tools and agentic development workflows
  • reason about systems you do not own well enough to predict how your changes affect them
  • Prefer influence through demonstrated work over positional authority

Nice to have

  • Have worked on business-critical enterprise platforms where financial correctness and auditability mattered
  • Have experience with Salesforce or comparable enterprise platforms, including their metadata and deployment models
  • Have built tooling or agents on top of an enterprise system of record
  • Have operated in a company under audit or compliance constraints

What the JD emphasized

  • Have built the delivery pipeline for a team, not just worked within one, and can describe the sequencing choices you made
  • Have led a team through a step change in engineering practice as its scope grew, including what you did when adoption slowed
  • Work fluently with AI coding tools and agentic development workflows, and have views on how a team should standardize around them

Other signals

  • build and shape the agentic tooling running on top of our platform
  • establish shared AI-assisted development standards for the team
  • work fluently with AI coding tools and agentic development workflows