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The Applied AI Engineering team at OpenAI helps enterprises deploy Codex to solve critical engineering workflows. You'll work directly with customer technical leaders and engineers to identify opportunities where AI can meaningfully improve how software is built, shipped, secured, and operated—spanning unfamiliar codebases, legacy systems, data pipelines, infrastructure, and enterprise integrations.
In this role, you will lead customer engineering engagements end-to-end: from workflow discovery and architecture through implementation, evaluation, and handoff. You'll assess customer repositories, data systems, infrastructure, and development practices to understand constraints and identify where Codex can have the greatest impact. You'll build and validate workflows alongside customer engineers, developing integrations, tool gateways, evaluation harnesses, reference implementations, and automations—often using Codex in your own development process.
You'll establish baselines, quality and security guardrails, and evaluation methods that demonstrate whether a workflow is reliable, useful, and ready to expand. A key part of the role is turning customer engagements into reusable tools, architectures, and deployment patterns, and translating recurring challenges into actionable feedback for product, engineering, and research teams. You'll document technical decisions and results for handoff and reuse, establishing clear ownership for production release, ongoing operation, and maintenance.
Success means delivering the agreed solution, demonstrating measurable improvement, and helping the customer sustain and expand its use. You'll use evidence to recommend when to expand a workflow, redirect an engagement, or stop work when value or feasibility cannot be demonstrated.
The position is based in Sydney, Australia, with three office days each week. Relocation assistance is available.
REQUIREMENTS:
- 5+ years of relevant experience in software development, deployment engineering, data or infrastructure engineering, technical consulting, or solutions architecture
- Ability to build, debug, and evaluate production-relevant software and quickly understand unfamiliar systems
- Active use of AI coding tools, adapted them to your own work, and have a clear perspective on how agents can automate complete workflows
- Technical depth in one or more areas such as software engineering, data systems, cloud infrastructure, or enterprise integrations, with willingness to learn across disciplines
- Understanding of how software is tested, released, and operated; ability to reason about security, reliability, and access requirements of enterprise environments
- Ability to turn ambiguous customer problems into practical technical plans with credible estimates, measurable acceptance criteria, and clear tradeoffs
- Effective communication with technical leaders and hands-on engineers; ability to collaborate through uncertainty and turn individual customer work into reusable solutions
