Canonical KPI
Pipeline generated, meetings booked, speed-to-lead
JD-mined KPIs (% of JDs that mention)
Pipeline generation (13%), data quality (13%), ARR (13%), qualified meetings (8%)
Pay context
Top payers: Anakin (YC S21) $800K · Cloudflare $446K · Anthropic $405K · Blacksmith $380K
Operator track $90–110K, Engineer track $135–155K — the $45K coding premium dominates this band.
Canonical KPI
AI roadmap, governance, vendor strategy, # workflows transformed org-wide
JD-mined KPIs (% of JDs that mention)
Adoption (27%), ARR (18%), pipeline generation (9%), seller productivity (9%)
Pay context
Top payers: Anthropic $550K · Databricks $424K · Elastic $396K · OpenAI $354K
Top of the market. Single-company outliers reach $450K+ for CRO-equivalent roles.
Canonical KPI
Content velocity, AI-agent output, AEO visibility
JD-mined KPIs (% of JDs that mention)
Activation (25%), data quality (15%), campaign performance (10%), adoption (10%)
Pay context
Top payers: Figma $296K · Vercel $296K · Salesforce $260K · Repl $250K
Most balanced stack in the dataset — splits TypeScript/React from Marketo/Salesforce roughly evenly.
Canonical KPI
Forecast accuracy, CRM integrity, system uptime, GRR/NRR (Revenue Architect tilt)
JD-mined KPIs (% of JDs that mention)
Data accuracy (15%), adoption (15%), ARR (15%), pipeline generation (11%)
Pay context
Top payers: Anthropic $405K · Higgsfield $300K · Anyscale $282K · OpenAI $265K
OpenAI alone runs 3 distinct $265K roles in this profile — the deepest single-company demand.
Canonical KPI
Everything revenue-adjacent; first revenue hire; repeatable playbook from $0
JD-mined KPIs (% of JDs that mention)
Adoption (27%), time-to-value (13%), campaign performance (13%), data quality (13%)
Pay context
Top payers: 14.ai (YC W24) $300K · Retell AI $260K · Solve Intelligence (YC) $250K · Welcome to the Jungle $225K
Equity is the real comp — 0.5–4% at YC seed, 0.1–0.5% at Series A. Top of band requires founder-grade equity.
Canonical KPI
Client retention, MRR on retainer, # systems shipped per engagement
JD-mined KPIs (% of JDs that mention)
Conversion rate (20%), MQL (20%), ARR (20%) — mostly client outcomes
Pay context
Top payers: RevOps.ai (Glossary) $450K · Preston Zeller (consulting) $300K · MarkOps AI (Maciek Marchlewski) $280K · Aquila (Fractional) $180K
Agency model = 6–8 tools per operator across clients. Solo CMO/GTM advisory model = different beast.
Canonical KPI
Activation, conversion, experiment velocity. Ships production code.
JD-mined KPIs (% of JDs that mention)
Activation (37%), adoption (33%), data quality (11%), CAC (7%), conversion rate (7%)
Pay context
Top payers: openart ai $400K · OpenArt AI $400K · OpenAI $385K · GC AI $350K
Genuinely an engineering role: 28% of JDs require React, 22% TypeScript. Pays like SWE.
Canonical KPI
Infrastructure shipped, MCP servers deployed, guardrails, agent reliability
JD-mined KPIs (% of JDs that mention)
Adoption (50%), AI adoption (18%), ARR (18%), CAC (9%)
Pay context
Top payers: Demandbase $284K · Elly Analytics $274K · OpenAI (highest pay, GTM innovation) $250K · Scale AI $224K
Highest LLM-skill density (27% mention LLM platform work). Adjacent to backend SWE comp.
Where the open roles actually sit
Total roles per archetype, segmented by current status. The Notion daily refresh reclassifies any "Likely Open" posting that closed overnight; this is yesterday's snapshot.
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Where the roles are
Every role with a parsable city, plotted at scale by open-role count. Remote / global roles get a separate counter — they aren't anchored to a place.
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Who's hiring GTM Engineers right now
Top companies with at least one open GTME role. Each tile's area is the open-role count; toggle the color to see archetype mix, AI fluency, coding intensity, or open-vs-closed share. Inspired by Karpathy's US Job Market Visualizer. Click any tile to open that company's roles in the job board.
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The titles vary, the shapes vary, but the substrate is the same. Eight flavors of one role family — all rebuilding revenue motion as code.
Read the full essay →Demand vs. supply by profile
Blue = open roles (demand). Gray = named practitioners (supply). Where blue is much wider than gray, the market is tight. Fractional / Consulting and Executive / CoE Lead are the only buckets where supply already outpaces visible demand.
How hard each archetype is to hire for
Open roles per known practitioner. Higher = harder to fill from the existing visible talent pool. Anything above ~2.0 means the discipline is faster than the supply can catch up.
| Profile | Open roles | Practitioners | Tightness | Read |
|---|---|---|---|---|
| AI Enablement Engineer | 38 | 19 | 2x | Demand outpacing supply |
| Founding GTME | 55 | 53 | 1.04x | In balance |
| Outbound GTME | 356 | 656 | 0.54x | Talent exists; titling lagging |
| AI Accelerator / Internal FDE | 28 | 54 | 0.52x | Talent exists; titling lagging |
| Executive / CoE Lead | 81 | 226 | 0.36x | Talent exists; titling lagging |
| Sales Engineer Hybrid | 29 | 87 | 0.33x | Talent exists; titling lagging |
| Marketing Engineer | 44 | 136 | 0.32x | Talent exists; titling lagging |
| Growth Engineer | 50 | 162 | 0.31x | Talent exists; titling lagging |
| Revenue Architect / RevOps Systems | 84 | 267 | 0.31x | Talent exists; titling lagging |
| Fractional / Consulting | 52 | 199 | 0.26x | Talent exists; titling lagging |
Which tools each archetype hires for
Each cell is the share of open JDs in that profile that explicitly mention the tool. RevOps Systems is 50% Salesforce. Outbound is 43% Clay. Marketo is the signature of the new AI Accelerator profile. React + TypeScript prove Growth Engineer is genuinely an engineering role.
| Profile | JDs | Clay | Salesforce | HubSpot | Marketo | Python | SQL | TypeScript | React | Claude |
|---|---|---|---|---|---|---|---|---|---|---|
| Outbound GTME | 356 | 42% | 42% | 29% | — | 22% | 20% | — | — | 18% |
| Marketing Engineer | 44 | 14% | 20% | 18% | 13% | 12% | 12% | 9% | — | — |
| Growth Engineer | 50 | 20% | 18% | 13% | — | 18% | 16% | 15% | 24% | — |
| Revenue Architect / RevOps Systems | 84 | 16% | 53% | 23% | — | 14% | 16% | — | — | 9% |
| Founding GTME | 55 | 42% | 26% | 23% | — | 20% | 21% | — | — | — |
| Sales Engineer Hybrid | 29 | 7% | 11% | 6% | — | 9% | — | — | 6% | — |
| Executive / CoE Lead | 81 | 7% | 16% | 8% | 5% | — | 3% | — | — | 4% |
| AI Enablement Engineer | 38 | — | 7% | — | — | 21% | 13% | — | — | — |
| AI Accelerator / Internal FDE | 28 | — | 17% | — | — | 40% | 26% | 15% | — | 13% |
| Fractional / Consulting | 52 | 37% | 16% | 28% | — | — | — | — | — | 8% |
Customer-facing flavors we track separately
The eight archetypes above are internal, on-the-business roles. We also track two adjacent customer-facing flavors that share the same substrate.
By the numbers, and where to dig in
Job board →
Search and filter every open role across the 10 profiles. Jump straight to the JD.
Practitioners →
2,347+ named people already doing the work. Search by company, archetype, or location.
Submit a role / profile →
Spotted a GTME role we missed, or want your profile listed? Submit it here.
How this is built →
The data pipeline behind /gtme — Notion as source of truth, sources, and caveats.
Based on a Notion-backed dataset of 1,879 job listings (871 open today) and 2,347 practitioners across 1,341 hiring companies, eight archetypes describe how the GTM Engineer is showing up in the market. Each tilts differently across AI fluency, GTM knowledge, data analytics, experimentation, and coding.
Roles come from job-board crawls (LinkedIn, Wellfound, YC, Built In, Teamtailor, Remote Rocketship) plus content sources (The Signal, GTMonly, State of GTM Engineering 2026, GTME Pulse, SyncGTM, Skaled). Each is classified into one of the eight archetypes via a deterministic flavor matcher and rated 1–10 across the five axes using keyword-mined heuristics. Practitioners come from public LinkedIn profiles already in role.