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GTM Engineer, Marketing

AI Summary
Tremendous is hiring a GTM Engineer to automate high-leverage marketing workflows using LLMs, AI agents, low-code tools, APIs, and scripting. The role partners with product marketing, brand, content, paid, events, and data teams to improve campaign execution, reporting, lead qualification, and HubSpot operations. The successful candidate will build reusable self-service tools rather than become a manual operations bottleneck. This is a senior, highly autonomous marketing-technology role in a fully remote, documentation-first environment. It requires practical experimentation and the ability to operationalize AI solutions quickly across a SaaS martech stack.
Remote from
USA
Salary
USD 135k–180k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
4 Dec 2026
Listing views
25
Application actions
3
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Opportunity details

About this role.

Tremendous is the global platform built for businesses to send thousands of payouts to anyone, anywhere, for free. We’re trusted by 20,000 leading organizations like Atlassian, MIT, and United Way to deliver gift cards and money to millions of recipients worldwide.

In both our product and our workplace, we’re intentional about making work more efficient, flexible, and fulfilling. Tremendous is a fully remote, high-documentation, low-meeting culture, which means more time for what matters in both your professional and personal life.

Our customers, who include researchers, marketers, HR teams, and nonprofits, rave about how fast and easy it is to use Tremendous. Check out our ratings on G2.

Tremendous is profitable and growing without outside investors. Join us before our next international offsite.

About the role

We’re looking for a GTM Engineer focused on marketing who can integrate LLMs, workflow tools, and low-code capabilities to build agentic solutions that automate the highest-leverage workflow problems our marketing team faces. This role will work alongside our brand, content, product marketing, paid, and events marketing teams.

What you’ll do

  • Design and build agentic workflows across the full campaign lifecycle, from Product Marketing to Design and Content to Demand Generation.

  • Evaluate and deploy AI tools for marketing. Run structured pilots, then operationalize what works from campaign ideation to execution and analysis.

  • Work with the Data team to build automated reporting pipelines that aggregate cross-channel marketing performance across paid, organic, email, and event channels into a unified view.

  • Build AI-assisted lead scoring and qualification with the marketing planner.

  • Let marketers run channel work from AI agents: paid campaign optimizations, creative uploads, and lifecycle campaign setup in HubSpot.

  • Build reusable tools and workflows that marketers can run themselves. Make the whole team faster rather than becoming a bottleneck.

What you’ll bring

  • 5-8 years in marketing operations, marketing technology, or GTM engineering. You understand campaign lifecycles and channel reporting because you’ve lived them.

  • You build the zero-to-one. Workflows stitched across tools where the inputs are messy and the judgment calls are real, rather than maintained applications on a custom code base.

  • Hands-on experience building with LLM APIs, AI agent frameworks, and workflow automation tools (Cargo, Zapier, Make, n8n, or similar). You’ve shipped agentic workflows, not just experimented with them.

  • Comfortable with APIs, webhooks, and basic scripting. You don’t stop when the no-code tool runs out of options.

  • Process-first thinker. Your instinct is to map the workflow and find where it breaks before naming the tool that fixes it.

  • Bias for rapid experimentation. Launch, measure, iterate weekly.

  • Strong written communication. We’re a documentation-first culture.

  • Autonomous and self-directed. Tremendous is not a great fit for people who default to waiting for instructions.

  • High empathy. You care about your teammates and users.

Nice to haves

  • Marketing-specific AI tools (Writer, AirOps, Phrase, etc.)

  • SaaS martech stacks (HubSpot, Salesforce, CDPs, ABM platforms)

  • SQL and Snowflake for reporting pipelines

  • Brand or creative operations background. You understand how designers work and what makes a good brief.

  • Cursor, Claude, or similar AI-assisted development tools

What’s cool about the role

  • Immediate, measurable impact. The problems are concrete and the wins are visible in weeks.

  • The team is already AI-forward. Cursor daily, monthly AI tool evaluations, leadership fully bought in.

  • Profitable and growing without outside investors.

  • Competitive pay, equity, and benefits. Base salary: $135,000 to $180,000.

  • We’re a fully remote company. This role is US Remote.

  • We’ve got smart people and a great culture. See our company handbook.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe role requires proven zero-to-one delivery of reliable agentic workflows across fragmented marketing systems, not merely experimentation with AI tools. Success depends on combining marketing operations judgment, technical integration ability, and rapid iteration with limited direction.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$157,500
US market range (per year)$130k–$190k
AI insightThe employer explicitly lists a yearly USD base-salary range of $135,000 to $180,000, with a midpoint of $157,500. Estimated US market compensation for a senior GTM/marketing automation engineer is approximately $130,000 to $190,000 per year; this is an AI estimate, not verified salary research.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an agentic marketing workflow you built from concept through production.

I would explain the business bottleneck, map the existing process and decision points, then describe the tools, APIs, prompts, human-review controls, and metrics used. I would quantify the outcome, such as reduced campaign setup time, better data quality, or increased qualified-lead throughput.

How would you design a unified cross-channel marketing performance reporting pipeline?

I would first define shared campaign taxonomy and the metrics needed by stakeholders. Then I would extract data from paid, organic, email, and event systems through APIs or connectors, normalize it in a warehouse such as Snowflake, validate data quality, and publish an automated dashboard with documented metric definitions.

How do you decide whether an AI tool should move beyond a pilot?

I set a specific workflow hypothesis, baseline time or quality metrics, and acceptable risk thresholds before piloting. I evaluate accuracy, adoption, cost, maintainability, and failure modes, then operationalize only tools that create measurable value with appropriate guardrails and ownership.

What would you do when a no-code automation cannot handle a required edge case?

I would isolate the constraint and use webhooks, API calls, lightweight scripting, or a custom service for the complex portion while retaining no-code orchestration where it remains useful. I would document the integration, error handling, monitoring, and fallback process so the workflow is sustainable.

How would you enable marketers to use AI workflows without creating governance risks?

I would provide clear workflow interfaces, role-based access, approved templates, validation rules, and human approval steps for high-impact actions. I would also document intended use, data-handling rules, monitoring metrics, and escalation paths so users can work independently within safe boundaries.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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