Applied AI Engineer, Digital Natives

About the Team

OpenAI’s Applied AI Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. We work with customer executives, product and engineering teams, security leaders, and transformation teams to identify valuable opportunities, accelerate technical implementation, and scale what works.

Production-ready deployments are defined by complexity rather than any one industry: existing architectures, diverse data environments, security and governance requirements, multiple stakeholder groups, and organization-wide change. We turn lessons from these deployments into better products and reusable patterns for customers everywhere.

About the Role

As an Applied AI Engineer you will partner directly with leading organizations to design, build, and deploy AI systems that deliver measurable business outcomes. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from use-case selection and architecture through prototyping, evaluation, production launch, and scale.

You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions involving model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact—not simply activity or successful demonstrations.

This is a rare opportunity to work on consequential real-world deployments at the frontier of AI while directly influencing how OpenAI’s products evolve.

This role is based in Paris. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees

In this role, you will:

Partner directly with enterprise and digital native customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria.

Design, build, and deploy AI systems that solve important customer problems and produce measurable business outcomes.

Work hands-on in code to build prototypes, evaluation harnesses, reference implementations, integrations, and production accelerators.

Make sound technical decisions across models, agents, retrieval, tools, data, reliability, observability, latency, cost, safety, security, and governance.

Diagnose complex implementation challenges, reproduce failures, test hypotheses, and drive blockers toward resolution.

Help customers progress from promising prototypes to reliable production systems, sustained adoption, and scaled impact.

Partner closely with customer engineering teams and OpenAI Product, Research, Engineering, Security, and go-to-market teams, translating deployment experience into high-signal product feedback.

Create reusable architectures, tooling, playbooks, and technical guidance that accelerate future enterprise deployments.

You might thrive in this role if you:

Have a demonstrated track record of designing, building, and delivering AI or machine-learning systems in enterprise environments, including taking systems from prototype to production. Relevant backgrounds may include applied AI or ML engineering, forward-deployed engineering, software engineering, customer engineering, solutions architecture, product engineering or technical consulting.

Are fluent in both French and English

Can point to substantial personal contributions in code, architecture, evaluation, debugging, or engineering of production systems—not only program or stakeholder management.

Are highly proficient in Python and comfortable working across an AI application stack; experience with JavaScript, TypeScript, or another relevant language is valuable.

Understand how to evaluate AI systems systematically using representative data, graders, production signals, and human judgment.

Have navigated enterprise production requirements such as integrations, reliability, observability, security, privacy, data governance, performance, and cost.