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GTM AI Engineer

Customer.io·August 21, 2026·
🌍 Remote · Americas RemoteFull-timeArtificial Intelligence

💰 $140,000 – $170,000/yr

At a glance

Who can apply
United States, Canada, Mexico, Brazil, Argentina, Chile +4 more
Work arrangement
Remote · Americas Remote
Employment type
Full-time
Salary
$140,000 – $170,000/yr · published by the employer

Checked against the employer's source on October 15, 2026. Details come from the employer's posting; confirm them on the application page.

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Job Description

About Customer.io

Over 9,000 companies—from startups to global brands—use Customer.io to send billions of emails, push notifications, in-app messages, and SMS daily. The platform powers automated communication using real-time behavioral data, helping teams send smarter, more relevant messages.

About the Role

Join Customer.io as a GTM AI Engineer to transform AI agents from scrappy prototypes into production-grade systems. Over 3.5 months, the GTM AI team has built 11 agents running in production, serving 120 active users and handling roughly 25,000 tool calls daily through custom CLIs. This role owns the scale and reliability layer, freeing the team to continue building.

Core Responsibilities

  • Alpha-to-production ownership—take newly built agents from v1 to production-ready: write evals, harden prompts and tool definitions, refactor prototype scaffolding into clean code.
  • Observability & stability—instrument tracing, logging, and monitoring across the agent fleet to catch regressions before users experience them.
  • Deployment & infrastructure—stand up and maintain deployment pipelines on GCP, owning environment parity and release hygiene.
  • Database administration—manage how agents safely read from and write to databases at scale.
  • Fleet maintenance—monitor production agent behavior, triage issues, and ship improvements that make agents indispensable.
  • Cross-team partnership—work closely with the GTM AI team while they focus on agent building.

What We Value

  • Craft over quick wins—energized by making things robust, not just functional.
  • A point of view—you ask 'why was it built that way?' and have specific answers for alternatives.
  • Comfortable being wrong—you try things fast and course-correct just as fast.
  • AI-native by default—you direct Claude to write and QA code rather than doing it by hand.

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