AI Deployment Engineer - Startups

OpenAI OpenAI · AI Frontier · Paris, France · Go To Market

AI Deployment Engineer focused on working with strategic startup customers to optimize their use of OpenAI's technology, identify failure modes, and translate learnings into product improvements and better model behavior. The role involves prototyping prompts/agents, designing evaluations, and building repeatable tools to improve AI systems.

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 fundamentals
  • Strong AI fundamentals
  • Experience building AI applications, agents, or evaluation systems
  • Ability to reason clearly about model behavior in complex workflows
  • Experience working directly with highly technical users
  • Ability to translate user challenges into concrete technical signals
  • Fluency in French

Nice to have

  • Experience as a startup CTO, software engineer, ML engineer, Data Scientist or equivalent
  • Experience shipping production systems end-to-end
  • Experience as a technical founder, or engineer at an early stage startup
  • Familiarity with, or interest in, model training pipelines and reinforcement learning
  • Ability to move fluidly between prototyping, debugging, evaluation design, and cross-functional collaboration
  • Clear communication across technical and non-technical audiences
  • High agency
  • Strong product sense
  • Bias toward building durable improvements rather than one-off fixes

What the JD emphasized

  • technical depth
  • strong product judgment
  • technically proficient
  • product-minded
  • push the frontier
  • complex workflows
  • reproducible evaluations
  • technical insights
  • ambiguous, high-impact problems
  • software engineering & AI fundamentals
  • shipping production systems end-to-end
  • technical founder
  • early stage startup
  • model training pipelines
  • reinforcement learning
  • building AI applications, agents, or evaluation systems
  • reason clearly about model behavior
  • highly technical users
  • prototyping, debugging, evaluation design
  • cross-functional collaboration
  • high agency
  • strong product sense
  • bias toward building durable improvements

Other signals

  • customer-facing
  • product feedback loop
  • evaluation systems
  • model behavior analysis