GTM Engineer: AI-Driven Revenue Automation (B2B)

CLEAR; New York (NY); On-site; USD 225,000 - 275,000; Full time

Benefits offered by this job

Healthcare plans

401(k) with employer match

Well-being stipends

Job summary

CLEAR is hiring a GTM Engineer to design and scale the systems, automations, and data infrastructure powering CLEAR’s B2B go-to-market organization. You’ll collaborate with Sales, Marketing, and Customer Success to identify friction points and deliver technical solutions that accelerate revenue growth.

You will own revenue systems architecture, build integrations across Salesforce and related tools, and prototype AI-enabled workflows to improve data quality, forecasting, and pipeline efficiency.

Qualifications

• 8+ years of experience in GTM Engineering, Revenue Operations, Sales Operations, Revenue Systems, Growth Engineering, or similar role in high-growth environments.
• Hands-on building in Salesforce, automation platforms, APIs, integrations, data tools, and emerging AI technologies.
• Ability to translate ambiguous business problems into scalable technical solutions with rapid prototyping, testing, and iteration.

Responsibilities

• Build and optimize GTM workflows across the funnel using automation, AI, and system integrations.
• Own and evolve revenue systems architecture across Salesforce and GTM tech stack, with workflows, integrations, reports, and tooling.

Skills

GTM Engineering

Revenue Operations

Sales Operations

Cross-functional leadership

Tools

Salesforce

Outreach

ZoomInfo

HubSpot

Gong

LinkedIn Sales Navigator

Workato

Tray

Job description

CLEAR is hiring a GTM Engineer to design and scale the systems, automations, and data infrastructure powering CLEAR’s B2B go-to-market organization. You’ll collaborate with Sales, Marketing, and Customer Success to identify friction points and deliver technical solutions that accelerate revenue growth.

You will own revenue systems architecture, build integrations across Salesforce and related tools, and prototype AI-enabled workflows to improve data quality, forecasting, and pipeline efficiency.