GTM Engineer, Applied AI

The Go-to-Market Engineer owns TLDR's GTM systems end-to-end: the input and interface layer where our team interacts with the revenue stack. This is a building role, not just maintenance - you'll decide what's worth quick-fixing, automating, or re-architecting, and bring an AI-first lens to problems RevOps teams have historically solved by hand. The role sits inside Applied AI, giving you real autonomy over a revenue-critical domain and a seat close to how TLDR builds AI into its GTM systems, work that directly supports our ~$35M FY26 revenue target.

In this role, you will:

Own GTM systems end-to-end: HubSpot administration, attribution, quota updates, new-product launches, quarter-over-quarter transitions, and CSAT plumbing.

Own data integrity: build pipelines right and keep fields accurate and flowing downstream.

Diagnose recurring problems, decide whether to quick-fix, automate, or re-architect, and build the fix.

Evaluate net-new systems (e.g., Sponsy alternatives) and recommend a path forward.

Bring an AI-first lens to classic RevOps problems like attribution, and partner with the Head of Applied AI on the broader AI × GTM operating model.

In your first 6 months:

You're the single accountable owner of the GTM systems ring, with the contractor-held work transitioned to you.

You've built the data-integrity layer - dashboards on field completeness and pipeline health, with alerting on top so no one checks by hand.

You've shipped your first wave of automations and agent workflows, each with monitoring attached.

You've documented it - fix-it procedures good enough that a break doesn't require you.

Main background: 4–6 years owning GTM systems at a B2B SaaS or B2B tech company - ideally from an early-stage (Series B–C) environment where you ran GTM systems end-to-end as a single owner

Primary skills: HubSpot ownership beyond admin; strong critical thinking and consultant-style communication; a product mindset for solution design balanced with pragmatic urgency; root-cause diagnosis paired with quick, clear proposals; hands-on data-integrity ownership (pipeline monitoring, field population, accuracy).

Secondary skills: Fluency in what AI-first GTM systems look like, applying AI to problems like attribution; familiarity with how APIs and integrations work.

Nice-to-haves: SQL

Want full ownership of a revenue-critical domain, with real autonomy and accountability, not just a task list.

Are comfortable operating without a paved playbook, this role has more ambiguity than structure.

Want to sit inside an Applied AI/engineering-focused team rather than a traditional RevOps team, and help define what GTM systems look like in an AI-native org.

🤑 Compensation : $175,000 - $205,000 + annual company performance bonus

🌎 Location : We're 100% remote across the US and Canada. Work where you want - our bands are set to tier-1 city rates wherever you live.

🤝 Team Events : Biannual team offsites. Most recently we’ve gone to New Orleans, San Diego and Park City!

🏝️ Time to Recharge : Flexible PTO. Most of the team takes 2–3 weeks a year, plus holidays.

🏥 Health Benefits : Comprehensive medical, dental and vision benefits with a 100% paid option

📈 401(k) Plan : Empower 401(k)

🍼 Paid Parental Leave

💻Home Office Stipend: Whatever makes you productive - standing desk, second monitor, chair, walking pad.

🔰 Learning & Development Stipend : For anything that makes you better at your work, and for AI tools especially. Don't ration your tokens.

📥 If you're ready to make a tremendous impact at a bootstrapped and profitable startup, please apply. Please let us know if you need any accommodation during any part of the interview process.

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