Sr. GTM Engineer, Inbound

SentinelOne

11d•$132,000 - $182,000•Remote

About The Position

As a Sr. GTM Engineer, Inbound, you will be tasked with a hands-on building role focused on the inbound funnel: how prospects find us, convert, get routed, and get nurtured, embedded with demand generation, digital, and marketing operations, shipping the AI agents, automations, and integrations that speed up lead response, sharpen lead scoring, and personalize the buyer journey from first touch to sales handoff. Success is measured in faster lead response times, higher conversion rates, and teams that no longer touch the manual version of this work. This is a senior individual contributor role where your leverage comes from what you ship, not from managing headcount, and you set the craft bar for inbound automation and coach marketers as you go.

Requirements

6 or more years across growth/GTM marketing, marketing/revenue operations, systems, or solutions engineering, including hands-on time building automation or AI into real GTM work.
Demonstrated experience designing, building, and deploying AI agents and agentic workflows that changed real work, not just using AI tools but building with them.
Fluency with the modern GTM stack and how data moves through it (CRM, marketing automation, enrichment, outreach, analytics), and comfort owning integrations and pipelines between them.
Strong prompt architecture skills and practical experience with agent frameworks, orchestration platforms (e.g., n8n, Make), and API/MCP integrations.
Working proficiency in at least one of SQL, Python, or JavaScript, enough to build lightweight tools, integrations, and workflows.
A builder's instinct paired with GTM judgment: you understand funnels, ICP, and buying signals, and you decide what is worth automating based on revenue impact, not novelty.

Nice To Haves

Experience building evaluation frameworks for AI systems (prompt evals, benchmarking, regression testing) is preferred.
Practical experience fine-tuning or heavily customizing models for production use cases is preferred.
Comfort coaching non-technical teammates through AI adoption and anticipating resistance without relying on positional authority is preferred.
A B2B and enterprise SaaS background, with familiarity with brand-safety and compliance constraints on automated outputs, is preferred.

Responsibilities

Speed up lead response. Build automations that route, score, and respond to inbound leads in minutes, not days. Own the systems behind speed-to-lead.
Build inbound AI agents. Design and deploy chat agents, form-follow-up agents, and lead-qualification agents that work the top of the funnel around the clock.
Personalize the conversion path. Build and integrate website personalization, dynamic forms, and content recommendations tied to visitor intent and account data.
Own the lead lifecycle stack. Integrate marketing automation, CDP, enrichment, intent data, and CRM so a lead's data and status stay accurate at every handoff (e.g., Marketo/HubSpot, Salesforce, Segment, ZoomInfo/Clearbit, 6sense).
Automate content and SEO ops. Build workflows that support content production, on-page optimization, and performance tracking at scale.
Make quality measurable. Build evals for lead-scoring accuracy, chat agent quality, and routing correctness against human baselines.
Build the guardrails. Implement human-in-the-loop review for anything customer-facing, plus logging and rollback paths.
Coach and document. Bring marketing ops and demand gen up the AI curve. Build playbooks and reusable templates.
Ramp across a clear arc: in months 1 to 3, embed with one or two priority teams, map their highest-friction workflows, and ship your first end-to-end automation with a real evaluation harness behind it; in months 4 to 6, expand to additional use cases, stand up reusable templates and a shared prompt and skill library, and start coaching the teams you have served toward running things themselves; in months 7 to 12, own a meaningful slice of the GTM stack and its agentic workflows, demonstrate measurable impact in cycle-time, output quality, and capacity, and hand off matured systems to their owning teams.
Own the year-one outcome: several of the highest-friction workflows in the teams you support running on systems you built and instrumented, those teams increasingly self-sufficient, a growing library of reusable and documented templates others build on, and provable quality, with an evaluation behind every deployed system and a rollback path in front of it.