Head of Go-to-Market · San Francisco

Build the commercial engine for a new market in AI compute.

Location New York City or San Francisco

Base $150,000–$200,000

Equity 0.10%–0.30%

Own the demand side end to end: map the market, close the transactions, and turn what works into a repeatable revenue engine.

Liquid Compute is building the financial layer for AI compute. GPU capacity is still bought and sold through fragmented, bespoke agreements: prices are hard to compare, supply is difficult to finance, and buyers and operators have few ways to manage volatility.

We are building one market across transparent reference indices, a derivatives exchange pending CFTC designation as a Designated Contract Market and registration as a Derivatives Clearing Organization, physical delivery, and credit. Our goal is to let market participants price, trade, settle, and finance GPU compute with the clarity expected of a mature asset class.

We are an early team backed by Y Combinator, working with AI companies, data centers, neoclouds, trading firms, and financial institutions. Joining now means helping define both the product and the market around one of AI’s most important inputs.

This is a hands-on commercial leadership role for turning early market interest into a repeatable revenue engine. The immediate priority is to understand buyer needs, create qualified pipeline, structure and close transactions, and become a trusted commercial counterpart for customers navigating GPU capacity and price risk.

You will own demand-side go-to-market while working closely with the founders and the compute-supply function on the other side of the market. Over time, you will turn what works into clear positioning, pricing discipline, repeatable sales and partnership motions, and a focused GTM team.

• Build demand and close revenue. Own the commercial cycle from market mapping and outbound through discovery, solution design, negotiation, close, and expansion.
• Make the market legible to customers. Translate reference pricing, physical capacity, and financial products into clear value propositions for AI companies, infrastructure operators, trading firms, and financial institutions.
• Lead complex transactions. Understand requirements across GPU type, region, duration, price, networking, storage, availability, and delivery structure; align technical, commercial, legal, and executive stakeholders around a workable deal.
• Create a repeatable pipeline engine. Build disciplined outbound, referral, ecosystem, event, and partnership motions, then establish pipeline stages, forecasting, deal reviews, follow-up, and win/loss learning.
• Shape product and pricing. Turn customer conversations and transaction data into decisions about customer segments, packaging, pricing, product priorities, and where Liquid Compute should focus next.
• Build the function deliberately. Stay close to customers and major deals while defining the roles, playbooks, and operating cadence a lean GTM team will need as volume grows.

• A clear market focus. Priority customer segments, use cases, messaging, and qualification criteria are explicit and used consistently.
• A credible revenue engine. The company has a healthy, forecastable pipeline, referenceable customers, and growing repeat transaction volume.
• A tight market feedback loop. Customer and deal insights reliably inform pricing, product, and supply decisions.
• A scalable commercial foundation. Core processes are lightweight but dependable, and the hiring plan follows demonstrated bottlenecks rather than org-chart ambition.

• You have built or materially scaled a 0-to-1 enterprise GTM or sales motion in AI infrastructure, cloud or neocloud services, fintech, marketplaces, commodities, or another…