Mercator

Mercator

Full-Stack Engineer

San Francisco

Work authorization requiredDetected 217 days ago
Next.jsFull-Stack DevelopmentTensorFlowAgentic AI

About the role

  • Today's systems leak $1.7T in waste each year in retail alone - in overproduction, lost revenue, and unnecessary emissions.
  • Mercator's founders are seasoned technical operators with a decade of experience leading cutting-edge AI and simulation efforts across Google (Gemini, Tensorflow, & Google X).
  • We recently raised $5M in seed funding from leading Silicon Valley investors and are scaling our small, elite engineering team to meet growing customer demand.

Responsibilities

  • Mercator is building AI agents that coordinate global supply chains.
  • As a member of our full-stack team, you'll build the interface that brings our simulation platform to life and redefines what usability looks like in a category historically dominated by Excel.
  • You'll design and ship consumer-grade experiences that make complex operational decisions feel simple, intuitive, and fast.
  • You'll collaborate closely with the engineer building our simulation engine to ensure advanced system output becomes clear, actionable, and trustworthy.
  • We are able to support H-1B transfers but are not initiating new visa sponsorships at this time.

Requirements

  • Applicants must have valid U.S. work authorization.

Benefits

  • Comprehensive health, dental, and vision insurance

Company info

  • skiing, surfing) two weeks a year
  • You'll work directly with customers to understand real workflows, translate them into modern product patterns, and own the full stack across Next.js and NestJS.
  • A critical part of the job is listening carefully to what customers ask for, recognizing when requests point in the wrong direction, and extracting the underlying workflow problem they're actually trying to solve.

Visa & Work Authorization

  • Applicants must have valid U.S. work authorization.

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