Founding GTM Engineer

Zingle AI Labs is an applied research lab for data agents, based in San Francisco. We're backed by Accel and Nexus, along with people from Anthropic, Scale AI, Cursor, MinIO, Hasura, and Postman. Reinforcement learning usually breaks down at the reward function. In most domains, deciding what "correct" means takes rubrics, human raters, and months of disagreement. Data is different. Correct means the numbers match: row counts, sums, null profiles, distributions. The reward is verifiable, binary, and already sitting in the warehouse. Data's hard problem was never the reward. It was the cost of an attempt. Checking a single change at production scale costs as much as the job itself, so agents got one try and a human did the rest. We made attempts cheap. Zingle gives agents a faithful scale model of a real warehouse (whole entities, full history, joins intact, per-entity numbers exact) and enough compute to run 100+ attempts an hour, each one checked against ground truth. Teams bring their own agents. We provide the environment and the verdict. Next, we're extending the same environment to train models for data work. We're currently live with 4 enterprises across the US. Data teams at top US fintech and sports-tech companies are building their own agents on top of us. About the role: You'll be our first GTM hire, working directly with the founders. You'll take a product that 4 enterprises already run on and turn it into a repeatable motion, from first conversation to signed contract to expansion. Own the full cycle with the founders: pipeline, discovery, pilots, procurement, close, expansion. Find the data and AI teams that are already building agents on their warehouse, and figure out exactly why they'd pay to stop guessing. Design pilots with clear success criteria, run them with engineering, and convert them to paid contracts. Build the playbook: ICP, messaging, outbound, events, partnerships. Write it down so the next five sellers can run it. Bring the field back into the product. What buyers don't trust, what blocks deployment, what they'd pay more for. You may be a good fit if you: Have sold technical infrastructure to data or engineering buyers. This is non‑negotiable. Have 7+ years in enterprise sales or GTM, with long cycles and multiple stakeholders (CDO, platform lead, security, procurement). Know the modern data stack well enough to whiteboard it: warehouses, dbt, Spark, orchestration. You can run a technical discovery call without an SE. Use AI coding agents yourself and can explain where they fail. Are comfortable being first. No SDRs, no enablement team, no playbook yet. Write cold emails that data leaders actually reply to. Strong candidates may also have: Early GTM experience at a data infra, devtools, or AI infra company A network of data and AI leaders at mid‑market and enterprise companies A track record of converting pilots or POCs into paid contracts Exposure to selling ML, evals, or AI platform products Early‑stage startup experience, ideally 0 to 1 Compensation and logistics: $200K – $250K base + 0.3% – 0.5% equity Direct work with data and AI leaders at Fortune 500 companies San Francisco preferred; remote US with regular travel to SF considered Location: San Francisco (preferred) or Remote, US #J-18808-Ljbffr