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Motive

GTM AI Engineer

United States - Remote (Remote)From $140kmidAdded today

About this role

Motive is hiring a GTM AI Engineer to design and deploy AI agents and full-stack applications for its 2,500+ go-to-market team. You'll own the complete lifecycle from prototyping through production, using tools like Glean, N8N, and Cursor to build solutions that drive measurable adoption and financial impact across SMB to strategic customer segments.

What you'll do

  • Own full development lifecycle of AI tools: prototyping, proof-of-concept, production scaling, beta testing, and maintenance
  • Build sophisticated agents and workflows using Glean, N8N, and Cursor across multiple LLM platforms
  • Collaborate with product managers and Enterprise Systems teams to securely integrate data sources while maintaining governance
  • Evaluate and recommend when agentic workflows are appropriate versus business-logic solutions
  • Manage cost envelope for production LLM usage, optimizing performance against token consumption
  • Document solutions and train end-users on new tools; stay current on LLM and orchestration advancements

What they're looking for

  • AI/LLM fundamentals: context windows, token consumption, prompt engineering, cost optimization
  • Agentic platforms: Glean, N8N, MCP, orchestration layers
  • Python and scripting for automation
  • SQL/SOQL and API integration
  • Full-stack development with Google Cloud and AI-enhanced IDEs (Cursor)
  • Foundation model expertise: Gemini, GPT, Claude
  • UX/product thinking for business users
  • Data governance and security practices
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Motive

Motive builds a fleet management platform that helps organizations track and manage their vehicles and operations. The company is hiring Field Engineers to lead hardware installations and deployments, providing technical expertise, partner training, and product feedback.

Website
motive.com
View all jobs at Motive

Likely interview questions

  • Walk us through a time you built an AI prototype and then scaled it to production—what were the key challenges?
  • How do you decide when to use an agentic workflow versus traditional business logic, and what factors influence that decision?