Applied AI Engineer, Quants

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. Enterprise 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 focused on quantitative investment and trading firms, you will partner directly with researchers, engineers and technical leaders to apply OpenAI’s models and Codex to their research and development workflows. You will combine an understanding of how quants work with hands-on technical skills, helping customers identify valuable opportunities, evaluate approaches and take successful experiments into production. You will work with both practitioners and senior leaders, connecting advances in AI to the problems that matter most to their firms. 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 London. 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: Work directly with quantitative investment and trading firms to identify opportunities across research, data analysis and software development, translating their needs into practical implementations, evaluations and measurable outcomes. 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. Lead technical workshops and hands-on sessions that help quantitative researchers and engineers apply OpenAI’s models and Codex effectively in their day-to-day work. Bring the needs of quant customers into OpenAI’s product development, translating deployment experience, evaluations and feedback into clear requirements for Product, Research and Engineering. Create reusable architectures, tooling, playbooks, and technical guidance that accelerate future enterprise deployments. You might thrive in this role if you: Have worked in quantitative research, quantitative development or a closely related role, or can demonstrate an excellent understanding of how quantitative investment and trading teams operate. You understand their research processes, technical environments and expectations for rigorous evaluation. Have practical, hands-on experience with LLMs, whether through tools such as Codex or through building AI applications. Deep experience deploying LLM systems is valuable, but strong quant expertise and the ability to learn quickly are the priorities. Can point to substantial personal contributions in code, architecture, evaluation, debugging, or production engineering—not only program or stakeholder management. Are highly proficient in Python and comfortable building and debugging research tools, data workflows or software systems. Bring a rigorous approach to evaluating quantitative, statistical or machine-learning systems, and can apply that discipline to assessing AI outputs and workflows. Have navigated enterprise production requirements such as integrations, reliability, observability, security, privacy, data governance, performance, and cost. Can connect technical decisions to customer workflows, adoption, and measurable business outcomes. Communicate with clarity and credibility across hands-on engineers, technical leaders, security teams, product leaders, and executives. Bring high agency, strong technical judgment, and end-to-end ownership in ambiguous environments. Learn quickly, challenge assumptions constructively and collaborate with humility. You are motivated to deepen your expertise in applied