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We’re hiring a Go-To-Market Engineer to build the internal systems that power how Magical identifies demand, engages buying groups, accelerates deals, and scales revenue.
This is not a sales role — and it’s not a traditional engineering role.
This role sits at the intersection of engineering, automation, AI, and revenue operations. You’ll design and ship the signal infrastructure, agent workflows, and orchestration tooling that turns messy GTM data into precise, automated action: from account intelligence and buying-stage detection to SDR alerts, personalization, and deal acceleration.
If you love building fast, automating everything, and seeing immediate business impact, you’ll thrive here.
Build GTM Signal Infrastructure - Design automated systems that score ICP fit, map buying committees, support visibility into buying group engagement and coverage, and track multi-threaded engagement across target accounts.
Capture Intent & Engagement at Scale - Aggregate and normalize intent, website, content, ad, and event signals to understand real buying behavior by account and persona.
Detect Buying Stage & Deal Health - Translate multi-signal engagement patterns into buying-stage readiness, momentum shifts, and stalled-deal risk indicators.
Orchestrate Automated GTM Actions - Trigger real-time outreach, personalization, audience syncing, nurtures, and deal-acceleration plays based on live engagement signals.
Build Internal Tooling & Agent Workflows - Ship AI agents, integrations, and automation tooling that turn manual GTM workflows into scalable revenue systems.
Partner With Marketing, Sales, and RevOps - Translate GTM strategy into scalable systems and automation.
Add Data Quality & Governance - Maintain data reliability, normalization, and signal accuracy across GTM systems
We’re looking for someone who is fluent in using AI to build. You should be comfortable:
Automating data enrichment & research workflows
Generating structured account intelligence from unstructured sources
This position is no longer accepting applications
Detecting anomalies or pipeline risk patterns
Summarizing engagement insights for GTM teams
Prompting effectively and building repeatable prompt workflows
Using tools like Claude Code, Codex, Cursor, or similar to build faster
Building multi-step agent flows and orchestrations
Working with RAG-style architectures (without needing to be an embeddings researcher)
Designing tool-calling agents that interact with APIs and internal systems
New grad or early-career engineer who has shipped real production code (internships, co-ops, side projects all count)
Alternatively, a marketer with strong technical skills who uses automation and AI to engineer scalable, signal-driven growth systems
In either case, you should have a strong scripting and automation comfort level (Python and/or TypeScript preferred)
Experience working with APIs, data pipelines, and integration-heavy systems
Builder mindset: you see a problem, you fix it, you ship it
High ownership, high urgency, low ego
Product instincts: you can think through user needs and design workflows that make sense
Interest in GTM and how companies actually scale revenue
Experience with browser automation tools (Playwright, Puppeteer, Selenium)
Experience building internal dashboards, admin tools, or automation frameworks
Experience building agent workflows or multi-agent systems
Experience with SaaS GTM (ABM, lifecycle marketing, sales pipelines, onboarding, implementation, CS workflows)
You need highly detailed specs before you can start
You prefer long planning cycles over shipping and iterating
You like research projects more than building real systems used daily
You want a slow-paced environment with heavy structure and oversight
Within your first 90 days, you will have shipped systems that:
automate GTM signal capture and orchestration
surface high-intent accounts earlier
improve SDR efficiency and deal velocity
reduce manual ops work across revenue teams
create durable automation leverage across GTM
increase signal reliability and GTM decision confidence
Within your first month, you’ll likely become the number 1 user of Magical’s own product, building and iterating with it daily while also creating tooling around it.
Work directly on problems tied to revenue and real-world adoption
Ship constantly and see impact immediately
Operate like a founder: high trust, high ownership, high velocity
Build the tooling layer that scales an AI company
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