Agent Systems Engineer, GTM & Internal Operations

Finite State partners with product security teams, the guardians of our connected world, to create transparency for their connected devices and supply chains. Our platform handles connected devices and embedded systems across all industries, including those found in enterprises, healthcare, utilities, connected vehicles, manufacturing facilities, critical infrastructure, and government entities. We are a fast-growing series-B company with a fully distributed workforce.

Led by a team of seasoned experts, we are a mission-driven team passionate about arming our customers with the actionable insights, critical vulnerability data, and remediation guidance necessary to mitigate product risk and protect the connected attack surface. We are committed to a remote first culture. S.

- based Department: GTM Ops The Opportunity Agent Systems Engineer is what we call this role. You may know it as GTM Engineer, Revenue Systems Engineer, Growth Engineer, or internal Forward Deployed Engineer. The substance is the same.

You’re a deeply technical operator who embeds with revenue teams, learns how the business actually moves, and rebuilds workflows as governed, AI-augmented systems. You write SQL fluently. You live in APIs.

You’ve shipped production GTM automation that other people depend on. You know what good revenue data architecture looks like (warehouse as source of truth, modeled in dbt, activated into Salesforce and the rest of the stack via Reverse ETL) and you have opinions about why most companies get it wrong. You also believe in practical AI.

You’ve deployed LLMs against real GTM problems where the business value was concrete (account research, classification, enrichment, content generation), and you have honest views about what worked, what didn’t, and where the hype outpaces reality. Most companies treating AI as a productivity tool are pointing it at individual jobs. We think the bigger opportunity is rebuilding entire revenue processes around agents and modern data infrastructure.

We sell that thesis to product security teams every day. Running our own GTM motion the same way is how we hold ourselves to the standard we’re selling. You’d be first in seat at Finite State, so you own the full motion: discovery with revenue leaders, system design, build, governance, evaluation, rollout, and the runbook so it survives you.

Responsibilities

Run discovery with revenue leaders before building. Sit in pipeline reviews. Watch a deal cycle end-to-end.

Find the actual time sinks before designing a solution Architect the GTM data layer: Snowflake (or equivalent) as source of truth, dbt for modeling, Reverse ETL (Hightouch, Census) for activation into Salesforce, HubSpot, Outreach, and the rest of the stack Design and build AI agents and AI-augmented workflows for revenue-critical work: account research, ICP scoring, signal-based plays, outbound personalization, CRM enrichment, deal intelligence, churn risk, expansion triggers, lead routing Deploy LLMs and agents where they add real business value, and skip them where they don’t. We’re not interested in AI for the sake of AI Wire agents and systems together via APIs, webhooks, MCP servers, and lightweight code (Python, SQL, TypeScript). Use platforms like Clay, n8n, Workato, or Hightouch AI when they fit.