GTM Engineer

Role Overview

ButterflyMX is seeking a GTM Engineer to own the automation and AI agent infrastructure of the Sales organization, building intelligent systems that eliminate manual friction, surface the right accounts and signals at the right time, and give reps and operators more leverage on every motion they run.

This role connects directly to selling capacity, market coverage, and pipeline velocity. The automation layer you build contributes to all three revenue constraints: freeing rep time from admin work, putting intelligence behind prospecting and pipeline management at scale, and accelerating deals already in the funnel. The GTM Engineer is the force multiplier that moves those levers. You are someone who thinks like a sales leader, works like a product manager, and builds like an engineer.

This is a senior individual contributor role that requires both technical depth and GTM fluency. You will operate with significant autonomy across the Sales automation domain, manage your own agenda against a stack-ranked set of revenue priorities, and be expected to identify what needs to be built, not just execute against a list.

When this role is working well, reps spend more time selling and less time on admin work, the right accounts are surfaced before reps have to go looking, pipeline data in Salesforce is reliable enough to trust in executive forecasting, and the Sales Ops team is shipping new agent capability on a regular cadence rather than fielding manual requests.

About You

• You eliminate revenue constraints, not just tasks. You sequence your work by the size of the bottleneck it removes — degraded pipeline quality, stalled deal execution, rep time lost to admin at scale. You find the root cause and build the agent that makes it stop.

• You build for maintainability, not the moment. You generate clean automations, document your integrations, and build agent workflows that others can operate and extend without you. Every automation is designed to run reliably and be improved over time.

• You understand the sales motion, not just the systems. You know how deals move, what rep behavior looks like at each stage, and where friction causes pipeline to stall or data to go missing. That fluency shapes what you prioritize and how you design automation that fits real workflows.

• You ship at sprint cadence. You take a prioritized backlog, sequence by revenue constraint size, and deliver working agents on a regular cycle. You scope tightly, ship fast, measure what changes, and iterate.

• You communicate in outcomes, not architecture. When you ship something, you tell reps and sales leaders what it does for them — hours saved, fields auto-populated, conflicts caught before they cost revenue, not how the integration works.

• You execute with ownership. Given a problem and context, you scope it, build it, and deliver it without being managed through it. You come back with what you shipped, what’s next, and where you need a decision.

Responsibilities

• Build and maintain AI-powered pipeline generation infrastructure — property trigger monitoring from third-party data sources; account and building scoring models; and outreach automation that operationalizes signals into rep-ready pipeline.

• Develop and operate a library of role-specific AI agents and prompt templates (i.e. post-call summarization, outreach personalization, next-best-action guidance, and deal coaching), while owning the prompt engineering and agent reliability infrastructure that keeps them running at scale.

• Design and build internal tools for reps and operators that give the Sales team self-serve access to intelligence and workflow acceleration.

• Analyze Salesforce data, call transcripts, pipeline patterns, and conversion signals to identify where revenue constraints are sharpest and which automation investments will move the most important levers; use those findings to set the automation agenda, measure the impact of what ships, and feed a continuous improvement loop between what gets built and whether it’s working.

• Own the rep-facing intelligence layer — the natural language query interface that gives reps and operators self-serve access to pipeline data, and the conversational intelligence pipeline that extracts Gong signal into CRM fields and surfaces actionable insights from call activity.

• Govern GTM data integrity across the Sales stack ensuring that Salesforce reflects activity accurately, opportunity stages are enforced before forecast pollution occurs, and account and building ownership conflicts are caught and resolved in real time.

• Maintain the integration and data quality relationships that keep the automation layer stable, working with Business Systems on integration reliability and with the Revenue Intelligence Lead to ensure agent inputs are clean, governed, and properly routed when issues surface.

Requirements

• Hands-on experience building and deploying AI agents using tools such as Claude, Codex, n8n, Zapier, Replit, or Lovable with agents that are adopted and actively used, not just shipped.

• Working knowledge of LLM orchestration, prompt engineering for GTM workflows, and maintaining prompt libraries and agent reliability in a live production environment.

• API and integration experience across core GTM platforms.

• Ability to build, debug, and own GTM automations using AI coding tools with sufficient engineering judgment to know when generated code is wrong or fragile, without software engineering support.

• Strong GTM and sales process fluency: how deals move, what reps do at each stage, and where the highest-friction bottlenecks live.

• Background in GTM engineering, revenue operations, business systems, or GTM tooling at a B2B SaaS company with a track record of shipping automation that measurably improves GTM performance.

Preferred

• Experience with both direct and channel/partner-led GTM motions, including automation that handles attribution, conflict detection, and routing logic across those sales tracks.

• Familiarity with property- or building-level account structures (with multiple stakeholders across owner, developer, property manager, and installer) and the Salesforce data modeling complexity that comes with it.

• Experience building automation across multiple sales segments or deal types with distinct signals, scoring criteria, and motion logic; for example, new logo and expansion motions for new development and retrofit customer segments.

• Exposure to real estate, PropTech, or construction intelligence platforms where property data and trigger monitoring are core to pipeline generation.