Head - AI Labs Partnerships

About

Built by second-time founders who have built and sold companies before, LH2 AI Labs is building the post-training infrastructure for frontier AI models.

We bring private, high-quality institutional datasets and vetted domain experts into frontier AI pipelines across verticals such as coding, computer use, agentic workflows, medical, audio, and more.

For AI to keep progressing, it needs high-quality training data drawn from real production use cases. The public web has already been crawled and trained on—there is limited new signal left there. That is where we come in.

Our vision is to create a world where frontier models can access high-quality data on tap, the same way they access compute today.

About the role

We’re looking for a senior sales leader who already has relationships inside the organisations we’re selling to - at the ML research, data, and partnerships level, not just at the C-suite. You know who at OpenAI is actually evaluating training data vendors this quarter across modalities. You’ve worked with the team at Anthropic’s data partnerships desk before. You understand what a frontier lab’s data team is looking for and how their procurement actually works.

This role is based in San Francisco or New York and requires frequent in-person travel - to frontier AI labs, model startups, enterprise AI buyers, agent builders, annotation platforms, and anyone else actively building with or on top of models. The buyer universe is broad: if they’re training models, fine-tuning agents, or running human data pipelines, they’re a potential customer. You’ll be in the room, not on Zoom.

The job is to turn those relationships into closed deals - and to surface what buyers actually need today so we can build or source it. You’ll report directly to the founders and work as a standalone function. It’s a pure sales role with a direct line to the top and real influence over what we build next.

Responsibilities

• Pipeline & Close
  • Own a named account list: frontier AI labs, frontier model startups, and enterprise AI buyers (think Turing, Scale AI, and similar)
  • Run the full sales cycle from warm intro to signed agreement - qualification, discovery, proposal, negotiation, close
  • Work directly with the founders on deal structuring, pricing, and partner conversations
  • Maintain a clean CRM - every contact, conversation, and stage, updated continuously
• Market Intelligence
  • Feed back what buyers are looking for - verticals, volumes, languages, modalities, compliance requirements, timelines
  • Flag emerging demand signals early enough that we can respond with supply, not just react to an RFP
  • Track competitive positioning - what Datacurve, Scale, and others are quoting and how buyers are evaluating them
• Human Data & Annotation Landscape
  • Map what’s happening in the human data and annotation market - who Scale AI, Turing, Surge AI, Labelbox, and Coactive are working with, what task types they’re running at volume, and where labs are hitting capacity or quality ceilings
  • Identify where our verified expert network can plug into annotation, RLHF, and evaluation workflows that annotation platforms are currently servicing - and where we have supply they don’t
  • Track how labs are shifting between synthetic data generation, human annotation, and hybrid pipelines - the balance is moving fast and directly shapes what they’re willing to pay for proprietary human-generated corpora
• Relationship Development
  • Own and deepen relationships with data leads, research leads, and ML partnerships contacts at target accounts
  • Stay current with org changes at frontier labs - who’s in seat, who’s moved, who’s the right contact now
  • Represent LH2 at relevant industry events and forums where buyers are evaluating data vendors

What We’re Looking For

• 5+ years in enterprise B2B sales, with at least some of that time selling into AI labs, research organisations, or data-intensive tech companies
• An existing network inside the organisations that matter - OpenAI, Anthropic, Google DeepMind, Meta, Cohere, Mistral, Microsoft Research, or comparable. Not warm introductions - real working relationships
• Fluent in how ML teams evaluate training data: what “provenance” means to a legal team, what “test coverage” means to a research team, what “de-identification” means to a compliance team
• Comfortable running a technically informed sales conversation without needing a technical co-pilot on every call