# Applied AI Engineer, GTM Growth Engineering

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $230k – $385k/yr
**Experience:** 7+ years
**Skills:** Python, LLMs, AI Agents, evaluation design, Experimentation, backend engineering, APIs, Data Pipelines, prompting, context construction, production monitoring
**Posted:** 2026-07-25

> Build and improve production AI agent systems for OpenAI's GTM workflows. Own the end-to-end improvement loop using feedback, evaluation, experimentation and backend services to drive measurable gains in customer engagement, pipeline and team productivity. Requires 4+ years building reliable LLM-powered production systems plus strong product judgment.

## Job Description

## Responsibilities
- 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.
- Design and ship targeted behavior improvements, including changes to prompting, context construction, decision logic, tool use, and human-review paths.
- Build backend services, APIs, data models, and feedback pipelines that make agent behavior observable, steerable, and reproducible.
- Run controlled experiments, production replays, or staged rollouts to measure whether changes improve quality and downstream business results.
- Partner with Product, Data Science, Sales, and B2B Marketing to prioritize high-value problems and define customer and business success.
- Ship with appropriate safeguards for privacy, security, reliability, human oversight, and safe operational rollout.

## Requirements
- 4+ years of software, backend, applied AI, or product-engineering experience building reliable production systems.
- Experience building AI agents, LLM-powered applications, or other model-driven workflows that operated on real production traffic.
- Experience diagnosing and improving agent behavior using production traces, user feedback, evaluation, experimentation, or careful systems design.
- Practical experience with evaluation design, regression testing, human or model grading, online quality signals, or controlled experiments.
- Strong backend engineering skills across Python, APIs, data pipelines, stateful workflows, and production services.
- Strong product judgment and the ability to connect technical changes to customer experience, conversion, qualified pipeline, or operational efficiency.
- Comfort working across model behavior, context, knowledge, tools, workflow state, and human-in-the-loop decisions.
- The ability to work closely with technical and non-technical partners across Engineering, Product, Data Science, Sales, and B2B Marketing.
- A pragmatic mindset: you can scope ambiguous problems, ship useful improvements, and build toward a durable system.

## Nice-to-Haves
- Experience building agent evaluation, observability, experimentation, or AI infrastructure products.
- Experience with production replay, LLM grading, human-labeled datasets, shadow evaluation, or staged rollout.
- Experience improving model or agent behavior through context design, prompting, tools, decision logic, or feedback loops.
- Experience with sales, B2B marketing, revenue, CRM, campaign, or other GTM-facing systems.
- Experience measuring customer engagement, qualified pipeline, conversion, or operational efficiency.

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