Netic

GTM Engineer

San Francisco

Sponsorship not specifiedDetected 135 days ago

Databricks

About the role

At Netic, we currently are laying the foundation for the best GTM engine in tech.
For our customers, we get to do the same.
If you're interested, send a 2 minute video of something you've built to noah@netic.ai.

Build the foundational internal tools, workflows, and automations for sales and marketing at Netic to use & leverage AI to be more effective
Build a flywheel based on customer conversations to accelerate our understanding of our users
Exceptional cross-functional skills. You build strong partnerships with Sales and internal partners to build systems that are aligned, high leverage, and easy to execute against.

Strong technical foundations - you can write some amount of code yourself and you understand how to set up the right architecture for systems that can scale
You have demonstrated experience moving the needle on the most important metrics you have owned and choosing the right set of initiatives to focus on

Netic is the AI revenue engine for essential services who are the backbone of the American economy.
With $43M in funding from Founders Fund, Greylock, Hanabi, and Dylan Field who led our Series B, we helped our customers book hundreds of thousands of jobs across services industries in North America.
There are now companies operating entirely AI-first on Netic.
You'll join our team with relentless builders from Scale, Databricks, HRT, Meta, MIT, Stanford, and Harvard in bringing frontier AI to the physical economy, where the problems are hard, the data is complex, and the impact is immediate and tangible.
refinement of first principles thinking, execution, and craftsmanship
We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
Come up with ways to use GTM Engineering principles to help our customers be more successful

This listing is sourced directly from Netic's careers page and normalized into a canonical job model.