GTM Engineer Intern
About the Role
We’re looking for an intern to help us build and implement AI-powered internal tools for our team. This role is focused on using AI to solve real internal problems - pulling information from multiple places, handling messy inputs, writing scripts, and building systems that our teams can actually use. Most of the work will involve coding/scripting and building AI-backed tools from scratch, with some portion involving workflow tools like n8n or Zapier. The work is exploratory by nature. You’ll be experimenting, building, testing, and improving AI systems that help GTM teams work faster and more efficiently.
What You’ll Do
Build internal AI tools that can be hosted and used by GTM teams.
Write scripts and systems that use existing AI models to solve real problems.
Work with data coming from multiple sources and formats.
Handle edge cases, ambiguity, and inconsistent inputs.
Use AI models to analyze information and generate useful outputs for internal use.
Use automation tools (like n8n / Zapier) where it makes sense.
Improve and iterate on tools based on internal feedback.
Collaborate with engineering and GTM stakeholders to understand problems and ship solutions.
Must-Have Skills
These are non-negotiable:
Python: Comfortable writing scripts and small programs.
Past AI project experience is mandatory. Academic projects, internships, hackathons, or strong personal projects are all valid. You should be able to clearly explain what you built, how AI was used, and why it mattered.
AI / Machine Learning fundamentals:
Understanding of how AI / ML models work conceptually.
Experience using existing AI or ML models (cloud or open-source).Ability to build solutions using existing models.
Comfort working with unstructured or semi-structured data.
Willingness to learn quickly and work in an experimental environment.
Nice-to-Have Skills
These are good bonuses, not requirements:
Experience with automation tools like n8n or Zapier.
Experience combining data from multiple sources into a single workflow.
Familiarity with scripting beyond Python.
Who This Role Is For
Experience with REST APIs, Basic knowledge of web hooks & backend concepts.
Someone interested in AI implementation, not just theory.
Comfortable with rapid experimentation and ambiguity.
Curious about how GTM teams operate and how AI can improve internal workflows.
Open to learning during the first few weeks and then owning real projects.
Who This Role Is NOT For
Someone looking only for academic ML research.
Someone without prior AI project experience.
Someone expecting a fully defined, non-experimental problem set.
Someone unwilling to build and iterate independently.
Internship Timeline
Weeks 1–2: Onboarding & Context
Understand how the company and GTM teams operate
Get familiar with existing internal AI tools
Review past projects and current workflows
Set up your development environment
Month 1: Working on Existing Systems
Contribute to maintaining and improving existing AI tools
Identify areas for improvement based on real usage
Ship enhancements end to end
Months 2-4: Owning some Internal AI Products/ProjectsScope and design an AI solution for a real internal problem
Build and deploy it end to end
Work with internal stakeholders to iterate
Months 5–6: Scaling & Advanced WorkImprove reliability and handle edge cases
Work on more complex integrations or other projects
What Success Looks Like
By the end of the internship, success means:
You’ve built and shipped multiple internal AI tools for GTM teams.
At least one tool is actively used by internal stakeholders.
You can independently take a vague problem, design an AI-based solution, and implement it.
Your work reduces manual effort or improves speed for GTM teams.
You’ve demonstrated strong ownership, learning ability, and execution.
What Happens After the Internship?
This is a fixed 6-month internship and does not automatically convert into a full-time role.
However, for interns who perform well and demonstrate strong execution and ownership:
Our engineering team will be interested in interviewing you for full-time roles when relevant positions open up.
There is no guaranteed conversion, but strong performance will be noticed and considered seriously.
Regardless of what happens next, you will leave with:
Hands-on experience building and shipping AI systems used by actual users
Practical exposure to designing and implementing applied AI workflows
Real project ownership in a production environment
Applied AI experience is in high demand across the industry, and the work you do here will strengthen your profile for similar roles across the market.