OpenAI
Applied AI Engineer, GTM Growth Engineering
About this role
Build and improve production AI agent systems that power OpenAI's go-to-market workflows. You'll own the full loop from understanding agent behavior in production to identifying failure modes, shipping improvements, and measuring business impact through customer engagement and conversion metrics.
What you'll do
- Own the production improvement cycle for AI agents: instrument workflows, collect feedback, evaluate performance, and validate business outcomes
- Design quality standards, evaluation datasets, regression tests, and production monitoring for GTM agent workflows
- Investigate agent underperformance across prompting, context, tools, routing, guardrails, and workflow design
- Ship targeted improvements including prompt refinement, context construction, decision logic, and human-review paths
- Build backend services, APIs, data models, and feedback pipelines to make agent behavior observable and steerable
- Run experiments, production replays, and staged rollouts to measure impact on quality and business results
What they're looking for
- Backend engineering (Python, APIs, data pipelines, stateful services)
- AI agent development and LLM-powered application experience
- Production system design and reliability
- Evaluation design, regression testing, and controlled experimentation
- Agent behavior diagnosis using traces, feedback, and telemetry
- Product judgment and business metrics alignment
- Cross-functional collaboration and communication
- Human-in-the-loop workflow design
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OpenAI
OpenAI builds AI infrastructure and products, including large-scale data center campuses for AI computing and generative AI applications for enterprise customers. The company is hiring civil engineers, project engineers, electrical design engineers, data center R&D engineers, and AI deployment engineers to expand its infrastructure capabilities and help customers deploy AI solutions.
View all jobs at OpenAILikely interview questions
- Walk us through a production AI system you've improved: what was the failure mode, how did you diagnose it, and what metrics proved your fix worked?
- How would you approach designing an evaluation framework for an AI agent that needs to perform well on real GTM workflows—what signals would matter most?