GTM Engineer

Anrok is the leading tax automation platform enabling businesses to expand globally without compliance complexity.

As the digital economy has grown 6x over the last decade, software businesses have gone from not worrying about sales tax to needing to monitor exposure, calculate rates, and file returns across 50+ US jurisdictions and many countries worldwide. This creates a critical bottleneck for companies that should be able to transact with customers everywhere.

Anrok eliminates this complexity by connecting with billing and payment systems to automate tax monitoring, calculations, and filing end-to-end. Our unified platform handles the ever-changing maze of tax laws at municipal, state, and federal levels—so companies can focus on growth, not compliance.

Our customers include:

• 40% of Forbes Top 50 AI companies

• 20% of Forbes Top 100 Cloud companies

• Top companies like Notion, Anthropic, and Cursor

We're making compliant digital commerce a reality for companies big and small, backed by over $100M from leading investors including Spark Capital, Sequoia, Index, and Khosla Ventures.

We are looking for a Go To Market (GTM) Engineer focusing on AI Adoption, who can code, build, and deploy intelligent and agentic systems that transform how our revenue team operates. You’ll sit at the intersection of software engineering and go-to-market strategy, building systems and automation workflows, deploying services on GCP, and integrating Claude into production pipelines that run reliably at scale. This is a hands-on technical role where you'll own the full lifecycle from writing code to deploying securely within our existing infrastructure—shifting our GTM motion from human-capacity-first to AI-first, directly impacting deal velocity, team productivity, and our ability to grow 3–4x without proportionally growing headcount.

• Design and execute the AI adoption strategy for the GTM team—identifying high-impact use cases for Claude, building reusable prompt libraries and AI agents, and establishing quality and security standards for LLM usage.

• Write and maintain production code (Python, JavaScript) that powers GTM automation workflows—from API integrations and data pipelines to Claude-powered agents that handle lead qualification, call analysis, and proposal generation, and more.

• Define and manage the deployment process for GTM-owned applications within a dedicated GCP project—including CI/CD pipelines, monitoring, logging, and security best practices for AI-powered services.

• Design and implement secure integrations between systems, such as Salesforce, Gong, Zendesk, GCP, and the Claude API—building robust data pipelines with proper authentication, error handling, and retry logic.

• Architect end-to-end automation workflows that eliminate manual work across the sales cycle—including signal-based lead enrichment and routing, post-call processing, pipeline management, and RFP response generation—building targeting infrastructure that compounds as the team scales.

• Create internal documentation, runbooks, and training content to drive team adoption; conduct hands-on sessions that help teammates incorporate AI tools into their daily workflows.

• Stay current on AI capabilities, GCP services, and industry best practices; recommend new tools and approaches that keep Anrok at the frontier of GTM productivity.

• 4 - 7+ years in a technical GTM, revenue operations, sales engineering, or software engineering role with hands-on experience building and deploying production systems.

• Strong coding skills in Python and/or JavaScript—you write clean, maintainable code, are comfortable owning services in production, and have fluency with APIs, webhooks, and integration patterns.

• Cloud infrastructure experience (GCP Preferred)—you’ve deployed and managed services using Cloud Functions, Cloud Run, BigQuery, or Pub/Sub, and you understand IAM, service accounts, and secure deployment patterns.

• Hands-on experience building with LLM APIs (Claude, GPT, or similar) in production—not just prompting, but integrating AI into reliable, monitored workflows with proper error handling and security controls.

• An owner's mindset with comfort in ambiguity—you take accountability from code to deployment to adoption, define scope with minimal direction, and ship good solutions rather than perfect ones.

• Comfort with ambiguity—you define scope and success criteria with minimal direction and understand that shipping a good automation beats perfecting one that never launches.