Forward Deployed Engineer - Sydney

OpenAI OpenAI · AI Frontier · Sydney, Australia · Model Deployment for Business

Forward Deployed Engineers (FDEs) lead complex end-to-end deployments of frontier models in production alongside strategic customers. This role involves discovery, technical scoping, system design, build, and production rollout, partnering directly with customer engineering and domain teams. Success is measured by production adoption, workflow impact, and feedback that influences product and model roadmaps. The FDE will contribute to both customer delivery and core platform development, working closely with various internal teams and potentially contributing code directly. The role requires strong engineering and deployment experience, particularly with LLMs or generative models, and the ability to manage complex systems in ambiguous environments.

What you'd actually do

  1. Own technical delivery across multiple deployments from first prototype to stable production.
  2. Build full-stack systems that deliver customer value and sharpen how we learn.
  3. Embed closely with customer teams, understand their needs, and guide adoption of what you build.
  4. Scope work, sequence delivery, and remove blockers early.
  5. Make trade-offs between scope, speed, and quality; adjust plans to protect delivery.

Skills

Required

  • 5+ years of engineering or technical deployment experience
  • customer-facing work experience
  • scoped and delivered complex systems in fast-moving or ambiguous environments
  • production-grade code across frontend and backend using Python, JavaScript, or comparable stacks
  • built or deployed systems powered by LLMs or generative models
  • understand how model behaviour affects product experience
  • Simplify complexity
  • make fast, sound decisions under pressure
  • Communicate clearly with engineers, product teams, and customer stakeholders
  • Spot risks early and adjust without slowing down
  • Model calm and judgment when the stakes are high

Nice to have

  • Python
  • JavaScript

What the JD emphasized

  • customer-facing work
  • customer delivery
  • production systems
  • production adoption
  • production rollout
  • production-grade code
  • customer teams
  • customer stakeholders
  • production

Other signals

  • customer delivery
  • production systems
  • frontier models
  • technical scoping
  • system design
  • production rollout
  • customer engineering
  • domain teams
  • production adoption
  • measurable workflow impact
  • eval-driven feedback
  • product and model roadmaps
  • Product
  • Research
  • Partnerships
  • GRC
  • Security
  • GTM teams
  • full-stack systems
  • customer value
  • customer teams
  • adoption
  • scope work
  • sequence delivery
  • remove blockers
  • trade-offs
  • scope
  • speed
  • quality
  • adjust plans
  • protect delivery
  • production-grade code
  • frontend
  • backend
  • Python
  • JavaScript
  • LLMs
  • generative models
  • model behaviour
  • product experience
  • simplify complexity
  • fast decisions
  • sound decisions
  • under pressure
  • communicate clearly
  • engineers
  • product teams
  • customer stakeholders
  • spot risks
  • adjust
  • slowing down
  • model calm
  • judgment
  • high stakes
  • AI research
  • deployment company
  • general-purpose artificial intelligence
  • AI systems
  • safely deploy
  • products
  • AI
  • powerful tool
  • safety
  • human needs
  • mission
  • perspectives
  • voices
  • experiences
  • humanity
  • AI potential
  • solve immense global challenges
  • upside of AI
  • widely shared
  • shaping the future of technology