GTM Engineer

Job not on LinkedIn

đź•’ 2 days ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

đźź  Senior

🧑‍💻 Full-stack Engineer

đź‘» Ghost score 12%

infoinfo

Apollo

SQL

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Logo of Togal.AI

Togal.AI

11 - 50 employees

Founded 2019

🏗️ Construction

đź’Ľ Consulting

🤖 Artificial Intelligence

đź’° $7M Seed Round - Togal.AI on 2023-03

Construction • Consulting • Artificial Intelligence

Togal. AI is a cloud-based, AI-powered takeoff platform for the construction industry that automatically detects, measures, compares and labels spaces and features on architectural plans and drawings. Built by estimators for estimators, it speeds pre-construction takeoffs, increases accuracy, enables real-time team collaboration, and automates manual tasks (including natural-language interaction via Togal. CHAT) to help contractors and subcontractors produce faster, more accurate bids.

đź“‹ Description

• Own systems that turn raw signals into pipeline, including prediction models, campaign infrastructure, and AI-generated personalized outreach • Build models for estimator count, seats, expected ARR, and conversion scoring, including confidence bands and ongoing retraining • Run multi-segment, multi-channel campaigns across email, LinkedIn, and voicemail • Own campaign code, routing, retries, deduplication, deliverability, uptime, and output quality • Generate and track per-rep landing pages and AI avatar video for end-to-end attribution • Develop call intelligence as a knowledge endpoint for prep documents, follow-up emails, avatar scripts, and page copy • Convert job changes, promotions, news, and clicks into scored, prioritized representative tasks • Inherit and improve live systems that already carry revenue • Make prioritization and implementation decisions under noisy data and imperfect attribution

🎯 Requirements

• 5+ years in growth engineering, RevOps, or a technical marketing or sales ops role tied directly to pipeline or revenue moved • Strong fluency in a programming language; ability to write, debug, and validate production code • Applied ML experience, including building, training, and maintaining a production model • Ability to explain model reliability to non-technical stakeholders • Experience with REST APIs, webhooks, OAuth, and rate limits • Strong SQL and data analysis judgment • Hands-on experience with HubSpot or Salesforce and tools such as Clay, Apollo, Instantly, and n8n • Practical LLM experience, including prompting, function calling, and output evaluation • Construction or AEC background, ML research background, frontend or backend depth, and experience scaling a GTM engineering function are bonuses

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