Vinci4D.ai

Vinci4D.ai

Head of Product Marketing

Palo Alto HQ

Sponsorship not specifiedDetected 27 days ago
Machine LearningSalesRobotics

About the role

  • As Head of Product Marketing, you will be responsible for connecting Vinci's technical capabilities to real engineering outcomes.
  • You will work closely with our physics, machine learning, product, and go-to-market teams to translate complex technology into clear value propositions, compelling customer narratives, and differentiated market positioning.
  • This role requires equal comfort discussing thermal reasoning, semiconductor packaging, and AI architecture with technical experts as it does discussing business impact with executives.

Responsibilities

  • Can quickly build credibility with engineers, researchers, and product teams.

Requirements

  • You have a talent for finding the connection between technical innovation and customer value.

Skills

  • The chips inside your phone.
  • The vehicles on the road.
  • The factories that make modern life possible.
  • The data centers powering artificial intelligence.
  • Yet most engineering decisions are still made with surprisingly limited access to physics.
  • As a result, engineers are forced to make critical decisions with only a partial view of how their systems will behave in the real world.
  • Vinci is building a Foundation Model for Physics.

Compensation

  • That includes competitive compensation, meaningful equity ownership, and comprehensive benefits.

Company info

  • Vinci is building technology that does not fit neatly into existing categories. As a result, one of our biggest challenges is not building the technology-it is helping customers understand what it enables.
  • As a result, one of our biggest challenges is not building the technology-it is helping customers understand what it enables.
  • Success in this role means making it dramatically easier for customers to understand, adopt, and champion Vinci.

This listing is sourced directly from Vinci4D.ai's careers page and normalized into a canonical job model.