Founding Forward Deployed Engineer

Sylvan Labs • New York, NY

11h • $180,000 - $250,000 • Onsite

About The Position

Sylvan is building autonomous revenue teams. We help B2B companies grow and protect revenue from their existing customers by identifying revenue-driving signals and deploying agents that act on them at the exact moment, across every account. We work with customers ranging from fast-growth startups to Fortune 500 enterprises like ServiceNow. Our ambition is to power the top-line of the economy.
We are guided by a core principle: no assholes, high mutual respect. We operate at high intensity, but with deep respect. Direct, honest, and no egos — just a shared obsession to win.
We’ve raised $10M+ from world-class investors and strategics including TQ Ventures and HubSpot Ventures. We are a fully in-office company based in New York City.
We will only hire A-players and are highly willing to go above-market in ownership, equity, and cash compensation. We want world-class builders who are excited by the opportunity to be part of a category-defining company at the earliest stage. It won’t be easy. We’re going to move fast. But, it’ll be fun.
The Role
You own a customer from initial data access through a working, trusted system: understanding their business objectives, constructing the context layer our agents run on, building and deploying the workflows, enhancing the agent harness as you learn what breaks, and then proving the whole thing delivers customer outcomes.
The hardest part up front is the data. Every warehouse is a mess in its own way: tables nobody documented, three competing definitions of ARR, and a data team with an hour a week to give you. Until that resolves into something clean and usable, nothing built on top is trustworthy.

Requirements

• You work at the frontier of agent-driven development. You've built your own setup rather than living on the defaults, and you keep finding ways to get more out of agents than they give most people.
• 3+ years in a technical, customer-facing role (forward deployed, solutions, or software engineering with heavy customer exposure); former founders strongly encouraged
• Deep data warehouse fluency. SQL, dbt, and real comfort in an undocumented five-year-old schema with nobody left to ask.
• You can sit with a customer's data or ops team, extract what a business concept actually means to them, resolve the cases where their own people disagree, and encode it.
• Applied data science literacy. Distributions, seasonality, validation design, calibration. You know when a result is noise.
• Painstaking through to the artifact. You'll chase a broken join for three hours because a number looked slightly off, and you'll care as much about whether the customer can actually interpret the output. Correct data in an unreadable form isn't delivered.
• You define success metrics rather than report on them, and you can build the case that the work delivered. You can also tell a customer their number didn't move and why.
• You hold a technical position under pushback from a customer or from us, and change your mind for evidence rather than authority.
• High tolerance for ambiguity and unglamorous work.
• Based in NYC, excited to be in-office.

Responsibilities

• Understand what the customer’s business objectives. Sit with the customer's revenue and ops leaders, get to the goals behind the request, and decide what is worth building against.
• Onboard new customers end to end. Scope data access, explore the warehouse with no documentation, and map a chaotic schema onto real business concepts.
• Stand up the ontology and verify the mapping. Interview the customer's data team and business owners, resolve their own inconsistent definitions, and validate the mapping before anything gets built on top of it.
• Build the signals and workflows on top of it. The predictions and actions the customer actually sees, and the agent harness behind them. When a step produces bad output for one customer, you diagnose it and improve it.
• Own the value case. Agree the success measure with the customer up front, then build the case that we delivered against it: the analysis, the evidence, the technical substance behind a story we can point to. You’re honest when the number didn't move, and specific about why.
• Make the next deployment faster. Validation frameworks, eval harnesses, reusable mapping tools — what you learn at one customer ships as tooling rather than tribal knowledge.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed