Go-To-Market Engineer

Automox

7h•$150,000 - $160,000•Remote

About The Position

Automox is seeking an innovative, results-driven Go-to-Market Engineer to build and operate hands-on, multi-channel marketing technology that supports predictable engagement and pipeline growth. This is a hands-on builder role on the Growth team in Marketing, supporting go-to-market teams across Sales, Professional Services, Alliances, and Marketing. The engineer will build AI tools, integrations, and data pipelines, primarily on AWS using Claude and other large language models. They will take requests directly from team leads, scope solutions, and own delivery end to end—from initial conversations through production deployment, training, and iteration. The role includes building internal applications and AI agents, hosting MCP servers that connect agents to Automox systems and business tools, running production LLM pipelines, and maintaining integrations and dashboards using go-to-market and analytics data. The engineer will also assess AI products and tools, support responsible adoption through security and governance review, and explain and document solutions for non-engineering users. Automox is a fully distributed company. The company currently does not hire in California or the New York metro area. Candidates must be able to pass federal, state, and county background checks and complete local in-person verification. Automox is unable to sponsor work visas at this time.

Requirements

Production software development experience with TypeScript and Python, including tests and CI/CD.
Experience putting LLM applications into production using Anthropic and OpenAI APIs, including prompt design, model selection, tool use, and token-cost management.
Experience building MCP servers and agent tools with SSO or OAuth sign-in and least-privilege access.
Experience with agent workflows that use evaluation and human review, including test sets, LLM-as-judge scoring, and approval steps.
Experience integrating APIs for go-to-market systems such as Salesforce, HubSpot, Gong, and Outreach.
SQL and PostgreSQL data-modeling experience, including embeddings and vector search.
AWS experience with ECS or EC2, RDS, S3, CloudFront, IAM, and GitHub Actions deployments without stored keys.
A track record of turning loosely defined requests from sales and marketing leads into scoped builds, then shipping and iterating.
Clear writing and documentation skills, and the ability to explain AI tools to non-engineers.
Sound security practices, including secrets management and access control, and experience with AI governance review before connecting a new tool.

Nice To Haves

Experience with Claude Code, Codex, Antigravity, Opencode, or similar coding agents, including skills, subagents, and project rules.
Experience with Next.js and React for internal tools, including generated PDFs.
Experience with BigQuery, GA4, and Google Ads data.
Experience with Okta SSO for internal applications and hosted MCP servers.
Infrastructure-as-code experience with CloudFormation or Terraform.
Experience with AI search visibility, including llms.txt, schema markup, and citability.
Background in IT operations, endpoint management, or security, such as patching and vulnerability response.

Responsibilities

Build internal applications and AI agents used by sales and services teams with customers.
Build and host MCP servers connecting AI agents to Automox systems, CRM data, and marketing tools.
Run production LLM pipelines for transcription, search, classification, and content generation.
Build and maintain data pipelines and dashboards using Salesforce, HubSpot, Gong, Outreach, and GA4.
Write Claude skills and automations for go-to-market teams and train users to use them.
Test AI products before launch and advise leadership on AI tools and proposals.
Take requests from go-to-market team leads, scope the work, deliver production solutions, and iterate on them.
Document solutions and explain AI tools to non-engineering users.
Apply security practices including secrets management, access control, and AI governance review before connecting new tools.