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OpenAI's Applied AI Engineering team helps organizations deploy frontier AI capabilities into safe, reliable, and high-impact production systems. This role focuses on quantitative investment and trading firms, where you will partner directly with researchers, engineers, and technical leaders to apply OpenAI's models and tools to their research and development workflows.
You will combine deep understanding of quantitative finance with hands-on technical skills to help customers identify valuable opportunities, evaluate approaches, and move successful experiments into production. You will work across the full stack: writing and debugging code, building evaluation systems, resolving complex integrations, and guiding decisions on model behavior, reliability, latency, cost, safety, security, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact.
Key responsibilities include:
- Work directly with quant firms to identify opportunities across research, data analysis, and software development, translating needs into practical implementations with measurable outcomes
- Design, build, and deploy AI systems that solve customer problems and produce measurable business impact
- 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 to resolution
- Help customers progress from prototypes to reliable production systems with sustained adoption and scaled impact
- Lead technical workshops helping quantitative researchers and engineers apply OpenAI's models effectively
- Bring customer needs into OpenAI's product development, translating deployment experience and feedback into clear requirements
- Create reusable architectures, tooling, playbooks, and technical guidance for future enterprise deployments
Requirements:
- Worked in quantitative research, quantitative development, or closely related role, with excellent understanding of how quantitative investment and trading teams operate, their research processes, technical environments, and expectations for rigorous evaluation
- Practical, hands-on experience with LLMs (through tools like Codex or building AI applications); deep LLM deployment experience is valuable, but strong quant expertise and ability to learn quickly are priorities
- Substantial personal contributions in code, architecture, evaluation, debugging, or production engineering—not only program or stakeholder management
- High proficiency in Python and comfort building and debugging research tools, data workflows, or software systems
- Rigorous approach to evaluating quantitative, statistical, or machine-learning systems; ability to apply that discipline to assessing AI outputs and workflows
- Experience navigating enterprise production requirements: integrations, reliability, observability, security, privacy, data governance, performance, and cost
- Ability to connect technical decisions to customer workflows, adoption, and measurable business outcomes
- Clear communication with credibility across hands-on engineers, technical leaders, security teams, product leaders, and executives
- High agency, strong technical judgment, and end-to-end ownership in ambiguous environments
- Quick learning, constructive challenge of assumptions, and collaborative approach; motivated to deepen expertise in applied AI
