About The Position
The RevOps team is evolving from a team that supports GTM tools into a team that builds the automation, data, and experimentation layer behind Telnyx’s growth engine. We are building AI agents, data workflows, internal tools, and MVP systems that help GTM teams identify opportunities, run experiments, validate ideas quickly, and scale the motions that work. This role sits at the intersection of RevOps, Growth, Sales, Marketing, Partnerships, Business Development, GTM Systems, and applied engineering. It is not a traditional sales ops role. It is a technical builder role for someone who can turn ambiguous GTM problems into working prototypes, testable workflows, and production-ready automations. We are looking for a Growth Experiment Engineer to build and validate MVP solutions for GTM growth experiments in weeks, not months. You will partner with Growth, Sales, Marketing, Business Development, RevOps, and GTM leadership to identify high-leverage experiments across pipeline creation, conversion, expansion, events, partner motions, product-led growth, outbound, inbound, and customer lifecycle workflows. Your job is to move fast: understand the growth hypothesis, build the smallest useful technical solution, instrument it, test it with real users or real data, and decide whether to scale it, iterate it, or shut it down.
Requirements
- Experience building technical GTM, RevOps, growth, sales ops, marketing ops, or automation systems
- Ability to prototype quickly using APIs, webhooks, structured data, SQL, lightweight scripts, automation tools, LLM workflows, or internal tools
- Strong understanding of B2B GTM motions: outbound, inbound, product-led growth, events, partner-led growth, expansion, pipeline creation, and handoffs
- Comfort figuring out unfamiliar tools, reading API docs, connecting systems, moving data, triggering workflows, and building MVP solutions quickly
- Technical judgment to choose the right level of build: manual test, spreadsheet, script, workflow automation, agent, internal tool, or production system
- Ability to define success metrics before building and evaluate results after launch
- Strong debugging instincts and comfort working through messy data, unclear requirements, and incomplete systems
- Clear communication with growth, sales, marketing, operations, and technical stakeholders
- Bias toward shipping, measuring, learning, and either scaling or killing experiments quickly
Nice To Haves
- Experience with Python, SQL, JavaScript, APIs, webhooks, or lightweight app/internal tool development
- Experience building AI-assisted prospecting, research, outbound, event follow-up, call analysis, or customer expansion workflows
- Familiarity with LLM workflows, prompt design, agentic systems, vector search, RAG, or AI-assisted GTM systems
- Experience designing growth experiments, funnel tests, campaign workflows, or signal-based GTM motions
- Experience with enrichment, attribution, intent signals, product usage data, event data, account scoring, or routing logic
- Experience working in telecom, infrastructure, SaaS, or developer-facing products
Responsibilities
- Build MVP systems, automations, agents, scripts, and internal tools that support GTM growth experiments
- Ship working prototypes in weeks, not months, then validate them with real users, real prospects, or real GTM data
- Translate growth hypotheses into technical workflows with clear inputs, outputs, success metrics, and failure conditions
- Create account research, enrichment, signal-monitoring, event follow-up, outbound-trigger, expansion, and product-led growth workflows using internal and external data sources
- Build AI-assisted workflows for research, routing, qualification, prioritization, follow-up, call analysis, and next-best-action recommendations
- Work across APIs, webhooks, structured data, automation tools, lightweight code, event data, usage data, data warehouses, and internal agents
- Instrument experiments so they can be measured quickly: conversion rate, meeting creation, opportunity creation, pipeline influenced, time saved, adoption, quality, or learning velocity
- Decide when to scale an experiment, when to iterate, and when to kill it
- Turn successful MVPs into repeatable workflows, durable automations, or specs for engineering/data partnership
- Document systems clearly so they can be operated, debugged, improved, and handed off
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
