Applied AI Engineer, GTM Growth Engineering

GTM Growth Engineering builds AI-native products that help OpenAI's go-to-market and B2B marketing organizations scale with greater speed, intelligence, and operational effectiveness.

We apply OpenAI models to real business workflows and build the systems that make those applications useful and dependable: customer context, agent behavior, feedback, evaluation, experimentation, and appropriate human oversight.

Our work brings together software engineering, applied AI, product, data, and GTM operations. We measure success through the quality of customer engagement, pipeline, conversion, and the effectiveness of our sales and marketing teams.

About the Role

We're looking for an Applied AI Engineer to build production systems that help AI-powered go-to-market workflows improve over time. You will connect agent behavior, customer and operator feedback, evaluation, experimentation, and business outcomes to make these systems more effective, reliable, and responsive to evolving customer needs.

This is a deeply technical, cross-functional role with end-to-end ownership of the agent improvement loop: understand production behavior, identify failure modes, improve how the system decides or acts, and validate the resulting impact.

You will partner with Engineering, Product, Data Science, Sales, and B2B Marketing to turn real-world signals into safer, more effective agent behavior and measurable improvements in customer engagement, conversion, qualified pipeline, and team productivity.

In this role, you will:

• Own the production improvement loop across agent behavior, customer and operator feedback, evaluation, experimentation, and verified business outcomes.

• Instrument agent workflows so model interactions, tool use, decisions, failures, human edits, and downstream outcomes can be understood in context.

• Define meaningful quality standards, representative evaluation datasets, regression coverage, and production monitoring for real GTM workflows.

• Investigate why agents underperform across context, knowledge, instructions, tools, routing, guardrails, or workflow design.