GTM Growth Engineer / Revenue AI Engineer
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- Find high-leverage GTM workflows and build the data/product foundation for measurable growth
- Build AI-native growth products, integrations and agent workflows that improve conversion and productivity
- Operate the growth loop with evals, experiments and verified revenue outcomes
Benchmark: OpenAI Applied AI Software Engineer + Software Engineer, GTM Growth Engineering; Anthropic Staff Software Engineer, Growth / GTM Systems and DevOps-AgentOps GTM Systems signals (current Sep 2026). Market refresh: AI-native GTM products; customer context; prioritization and routing; full-stack systems; agent behavior; CRM/martech integration; feedback and eval loops; experimentation; human oversight; pipeline, conversion and seller/marketer productivity.
Who’s Hiring Right Now
US openings posted in the last 7 days at Anthropic, OpenAI — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Find high-leverage GTM workflows and build the data/product foundation for measurable growth
Workflow discovery, customer context + growth problem framing
Coverage: GTM Systems / Customer Discovery / Revenue Operations / Product Discovery / AI Use-Case Scoping
GTM data, SQL + measurable funnel instrumentation
Coverage: SQL / Customer Data / Funnel Metrics / Attribution / Product Analytics / Data Quality
Build AI-native growth products, integrations and agent workflows that improve conversion and productivity
Full-stack GTM products + APIs/integrations
Coverage: Full-Stack Engineering / APIs / CRM & Martech Integrations / Data Models / Workflow Orchestration
Agent behavior, tools + human-supervised automation
Coverage: AI Agents / Tool Use / Context Engineering / Human-in-the-Loop / GTM Automation
Operate the growth loop with evals, experiments and verified revenue outcomes
Agent evaluation, feedback loops + production improvement
Coverage: AI Evals / Telemetry / Failure Analysis / Regression Gates / Operator Feedback / Monitoring
Experimentation, rollout + business-impact measurement
Coverage: Experimentation / A-B Testing / Staged Rollout / Adoption / Pipeline / Conversion / Revenue Productivity
Role-Readiness Proof / Capstone
Build an AI-native GTM growth system for a simulated B2B product. Interview sales/marketing operators and identify one high-friction revenue workflow; define customer context, lifecycle states and measurable success metrics; create the required CRM/data model and SQL baseline; ship a full-stack product or service that prioritizes, routes or assists customer engagement; integrate at least one CRM/martech system plus one tool through APIs/MCP; instrument agent traces, human edits, funnel outcomes and cost; build representative evals and regression gates; run an experiment or staged rollout; then quantify changes in qualified pipeline, conversion, response time or operator productivity and document privacy, safety, rollback and human-approval boundaries.
Proof Rubric
- Demonstrate user and operator evidence
- clear revenue problem selection
- reliable full-stack implementation
- trustworthy customer/context data
- robust API and tool integration
- safe human-supervised agent behavior
- eval and regression coverage
- telemetry and diagnosable failures
- disciplined experimentation
- measurable pipeline/conversion/productivity impact
- privacy/security controls
- cost and latency awareness
- and evidence that production feedback changed the system rather than merely confirming the original design