AI Deployment Engineer, Startups

OpenAI OpenAI · AI Frontier · Sydney, Australia · Go To Market

AI Deployment Engineer focused on working with strategic startup customers to optimize their AI systems, translate insights into product improvements, and design evaluations. This role involves prototyping prompts, agents, and workflows, and synthesizing feedback for OpenAI's research and product teams.

What you'd actually do

  1. Work directly with strategic startup customers to understand critical workflows, uncover failure modes, and identify high-impact opportunities for improvement.
  2. Prototype and iterate on prompts, agents, and workflow designs to better understand system behavior and unlock customer value.
  3. Synthesize and deliver valuable feedback to the Product and Research teams, turning real usage patterns into clear, reproducible evals, benchmarks, and technical artifacts that improve model and product quality and ensure customer-grounded learnings influence roadmap and model development.
  4. Build repeatable tools, patterns, and evaluation approaches that raise the quality bar across multiple use cases.
  5. Operate with strong judgment in ambiguous environments, balancing immediate technical problem-solving with longer-term system improvement.

Skills

Required

  • strong software engineering & AI fundamentals
  • experience shipping production systems end-to-end
  • familiarity with model training pipelines
  • familiarity with reinforcement learning
  • experience building AI applications
  • experience building agents
  • experience building evaluation systems
  • reason clearly about model behavior in complex workflows
  • comfortable working directly with highly technical users
  • translating their challenges into concrete technical signals
  • move fluidly between prototyping, debugging, evaluation design, and cross-functional collaboration
  • communicate clearly across technical and non-technical audiences
  • high agency
  • strong product sense
  • bias toward building durable improvements

Nice to have

  • iterating on prompts
  • designing evaluations

What the JD emphasized

  • push the frontier of advanced AI
  • partner deeply on complex workflows
  • identify the gaps that matter
  • transform those gaps into reproducible evaluations
  • technical insights
  • shape OpenAI's research and product direction
  • ambiguous, high-impact problems
  • shape how advanced AI systems improve in practice
  • shipping production systems end-to-end is a strong plus
  • building AI applications, agents, or evaluation systems
  • reason clearly about model behavior in complex workflows
  • translating their challenges into concrete technical signals
  • move fluidly between prototyping, debugging, evaluation design, and cross-functional collaboration
  • high agency
  • strong product sense
  • bias toward building durable improvements rather than one-off fixes

Other signals

  • customer-facing technical advisor
  • optimize AI systems
  • translate insights into product improvements
  • design evaluations
  • prototype and iterate on prompts, agents, and workflow designs