GTM Engineer

Vaga não está no LinkedIn

🕒 6 dias atrás

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

👻 Score fantasma 12%

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🗣️🇺🇸🇬🇧 Inglês obrigatório

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Logo of Togal.AI

Togal.AI

11 - 50 funcionários

Fundada em 2019

🏗️ Construção

💼 Consultoria

🤖 Inteligência Artificial

💰 $7.000.000 Seed Round - Togal.AI em 2023-03

Construction • Consulting • Artificial Intelligence

Togal. AI é uma plataforma de takeoff baseada em nuvem com tecnologia de IA, voltada para a indústria da construção, que detecta, mede, compara e rotula automaticamente espaços e características em plantas e desenhos arquitetônicos. Desenvolvida por estimadores para estimadores, ela acelera os takeoffs pré-construção, aumenta a precisão, permite a colaboração em tempo real da equipe e automatiza tarefas manuais (incluindo interação em linguagem natural via Togal. CHAT) para ajudar empreiteiros e subcontratados a produzirem propostas mais rápidas e precisas.

Descrição

• 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

🎯 Requisitos

• 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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