Founding Enterprise GTM Lead

Posted Yesterday
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Palo Alto, CA, USA
Hybrid
Expert/Leader
Artificial Intelligence • Computer Vision • Machine Learning • Generative AI
The Role
Owns enterprise commercial strategy and execution for a frontier multimodal AI company. Responsibilities include closing complex seven- and eight-figure technical deals, defining value propositions and target customers, designing pricing and packaging, converting technical pilots, and building repeatable go-to-market processes. The role partners with research and engineering teams, influences product roadmaps, and establishes the foundation for scaling the future commercial organization.
Summary Generated by Built In
About Us

We are a well-funded, early-stage AI research and product lab focused on building the next generation of frontier multimodal AI models. Founded by former DeepMind researchers—including Andrew Dai, previously an engineering leader on Gemini—our mission is to push the frontier of visual and multimodal intelligence. Our first model, Glimpse, launched publicly in October 2026.

Our team currently consists of 25 world-class scientists and engineers. We raised a $55M seed round led by Altimeter Capital, Menlo Ventures, Striker VC, and NVIDIA. We are tackling foundational problems in artificial intelligence and translating cutting-edge multimodal research into scalable, real-world systems.

About the role

We are building foundational AI systems for technical teams to build upon. Deal size, pilot momentum, and pipeline complexity have accelerated, and we are looking for our first dedicated enterprise commercial leader.

As our Founding Enterprise GTM Lead, you will be the strategic architect and primary execution driver of our entire commercial engine. In the near term, you will personally own and close our largest, most complex enterprise contracts, navigating security, legal, procurement, and executive negotiation end-to-end. Simultaneously, you will build our 0-to-1 Go-to-Market foundations: defining our core value proposition, carving out target buyer personas, packaging our model capabilities into commercially viable products, and setting up the repeatable sales motions that will underpin our long-term scale.

What you will do
  • Define Value Propositions & ICP: Translate frontier multimodal research into compelling, ROI-driven business narratives, customer segmentation frameworks, and competitive positioning against incumbent foundation models.

  • Lead Full-Cycle Flagship Deals: Personally quarterback high-stakes enterprise sales from outreach through InfoSec, procurement, and legal negotiation to close 7- and 8-figure contracts.

  • Architect Pricing & Packaging: Design and formalize hybrid commercial models balancing platform access fees, dedicated compute reserves, and consumption-based token pricing.

  • Convert High-Value Technical Pilots: Partner with Research and Forward-Deployed Engineering to scope paid PoCs with quantitative performance benchmarks and pre-negotiated production conversion terms.

  • Drive R&D Feedback Loops & Scaling: Synthesize enterprise market requirements to shape product and evaluation roadmaps while establishing the playbook to hire and scale our future commercial team.

What we are looking for
  • Enterprise Closer with Strategic GTM Acumen: Proven track record of carrying a quota and closing complex, seven-figure technical enterprise deals, combined with experience crafting 0-to-1 GTM strategy.

  • Value-Led Technical Positioning: Demonstrated ability to translate raw technical primitives and frontier model benchmarks into clear business cases and strategic value propositions for C-level buyers.

  • Model & Infrastructure Literacy: Deep fluency selling developer-centric or infrastructure products (APIs, foundation models, compute platforms, or ML tooling) embedded directly into core technical stacks.

  • 0-to-1 Builder Mentality: Comfort navigating ambiguity without existing playbooks, collateral, or enablement teams—thriving while building pitch decks, MSA redlines, and sales processes from scratch.

  • Cross-Functional Technical Gravitas: Ability to command respect with enterprise CTOs and InfoSec teams while collaborating seamlessly with forward-deployed engineers.

Nice to have
  • Vertical Domain Depth: Existing enterprise network and domain fluency in high-potential multimodal verticals such as autonomous systems, robotics, video/media intelligence, medical imaging, content safety, or gaming.

  • Usage-Based Commercialization: Direct experience structuring, modeling, and closing hybrid usage/consumption-based pricing structures alongside annual platform commitments.

  • Early-Stage Foundation Model Experience: Prior experience as a founding or early GTM hire at an AI/ML research lab, developer platform, or frontier model startup.

Skills Required

  • Proven track record carrying a quota and closing complex seven-figure technical enterprise deals
  • Experience crafting zero-to-one go-to-market strategy
  • Ability to translate technical primitives and model benchmarks into business cases for C-level buyers
  • Deep fluency selling developer-centric or infrastructure products, including APIs, foundation models, compute platforms, or ML tooling
  • Comfort building sales materials, MSA redlines, and sales processes from scratch
  • Ability to work effectively with enterprise CTOs, InfoSec teams, and forward-deployed engineers
  • Enterprise network and domain expertise in multimodal verticals such as autonomous systems, robotics, video intelligence, medical imaging, content safety, or gaming
  • Experience structuring usage-based or hybrid consumption pricing with annual platform commitments
  • Prior founding or early go-to-market experience at an AI/ML research lab, developer platform, or frontier model startup
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The Company
18 Employees
Year Founded: 2025

What We Do

Elorian is an early-stage AI research lab founded by former DeepMind and Apple researchers. They are focused on building the next generation of frontier multimodal AI models with a specific emphasis on advanced visual reasoning. By moving beyond text-centric LLMs, they aim to enable AI to understand the spatial and structural complexity of the physical world, pushing the boundaries toward Artificial General Intelligence (AGI) for applications in robotics, science, and industry.

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