Hyperbound

Hyperbound

GTM Engineer

San Francisco

Sponsorship not specified$150k-$180kDetected 19 days ago
JavaScriptPythonData StructuresSQLRESTdbtData EngineeringAgentic AIHubSpotCRMGrowth MarketingSalesOutbound SalesResearchLeadershipMentoring

About the role

  • You sit at the intersection of revenue operations, software, and growth, writing the workflows, queries, and automations that connect enrichment, scoring, routing, and outreach into one system that runs at scale.
  • You think in systems, not campaigns: equally comfortable designing how revenue flows from first touch to expansion and debugging a broken sync or tuning an enrichment waterfall.

Responsibilities

  • Build automated GTM systems. Ship workflows that capture, enrich, score, route, and sequence leads and accounts in Clay and the rest of the stack, replacing manual research with infrastructure that runs unattended.
  • Own data and the stack. Tune waterfall enrichment across providers, own dedup and CRM data integrity, and integrate HubSpot, Amplemarket, Warmly, Clay, Vector, and Zapier via APIs and webhooks.
  • Deploy AI into the workflow. Build AI-driven research, drafting, and agent workflows with the right human-in-the-loop checks, and monitor them so quality holds as volume scales.
  • Architect the lead-to-cash motion. Design routing, scoring, lifecycle and handoff logic, and deal triggers across marketing, sales, and CS, and remove the bottlenecks that slow revenue.
  • Hands-on experience building automation in Clay: enrichment, scoring, routing, and outbound at scale
  • Strong grasp of the revenue lifecycle: ICP definition, lead routing and scoring, pipeline hygiene, and CRM data structure
  • Build automated GTM systems.
  • Ship workflows that capture, enrich, score, route, and sequence leads and accounts in Clay and the rest of the stack, replacing manual research with infrastructure that runs unattended.
  • Onsite/final with Sai and Atul (Co Founders), Lisette (Growth Marketing Lead), Mason (Chief of Staff), and James (Founding Recruiter)
  • Build reporting that drives decisions.

Requirements

  • Familiarity with data warehouses, reverse ETL, or dbt
  • Experience with Partnerstack, Crossbeam, MEDDPICC, or an ABM motion

Nice to have

  • API literacy and comfort writing SQL, plus scripting in Python or JavaScript for custom integrations
  • Deep, hands-on HubSpot expertise across CRM administration, automation, and reporting

Compensation

  • Compensation: $150k - $180K+ based on experience and location

Benefits

  • Medical, dental, and vision coverage
  • Own the dashboards and metrics (pipeline health, conversion, velocity) that leadership needs to make calls.
  • Equity:.04%-.08%, Series A options
  • Time off: Unlimited PTO
  • Other: Commuter benefits, paid lunch in the office
  • We do not discriminate on the basis of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Company info

  • Hyperbound (YC S23) is the Revenue Activation Platform, an agentic OS for sales that closes the loop between behavior, coaching, and execution.
  • We're a system that changes what happens next, and we're reshaping the structure of our customers' sales orgs in the process.
  • As the inventors of AI sales roleplay, we help enterprise sales teams practice, measure, and scale top-performer behaviors.
  • IBM, LinkedIn, Bloomberg, Supabase, Monday.com http://Monday.com, Notion, and Vanta are just a few of the companies that trust us.
  • We 5x'd ARR last year and raised a $15M Series A led by Peak XV.
  • Our team ships new features weekly and has close feedback loops with customers.
  • The category is exploding, and we're pouring gas on the fire.

Equal opportunity

  • Hyperbound is an equal opportunity employer.
  • We welcome applicants of all backgrounds, identities, and experiences.
  • If you need accommodations during the interview process, let us know and we will make it work.

This listing is sourced directly from Hyperbound's careers page and normalized into a canonical job model.