About this role
Quantitative investment and trading firms frequently struggle to bridge the gap between frontier artificial intelligence capabilities and the complex security, latency, and governance requirements of live production environments. To address this challenge, OpenAI requires an Applied AI Engineer focused on quants to bridge the gap between model research and practical industry implementation.
In this position, the engineer collaborates directly with researchers, developers, and technical leaders within financial institutions to transition successful experiments into reliable systems. Responsibilities include writing code, building evaluation frameworks, and guiding architectural choices concerning latency, cost, and security.
The ideal candidate combines a strong working knowledge of quantitative workflows with hands-on software engineering capabilities in Python and large language models. Individuals who thrive here possess high agency, a rigorous approach to system evaluation, and the ability to navigate ambiguous enterprise environments with strong technical judgment.
What you will do
- Partner with quantitative trading firms to map out requirements across research and software development, translating their needs into measurable outcomes.
- Design and deploy production-ready artificial intelligence systems that address critical customer workflows.
- Write hands-on code to construct prototypes, evaluation harnesses, and software integrations.
- Drive technical decisions regarding model reliability, latency, cost, security, and governance.
- Diagnose implementation bottlenecks and resolve blockers to move prototypes into sustained production.
- Lead technical workshops and interactive sessions to help quant researchers apply language models effectively.
- Translate deployment feedback from financial clients into clear feature requests for product and engineering groups.
What you need
- Demonstrated experience in quantitative research, quantitative development, or a strong understanding of how trading teams operate.
- Practical, hands-on experience utilizing large language models or building artificial intelligence applications.
- Demonstrable personal contributions in code, system architecture, evaluation, or production engineering.
- High proficiency in Python with the ability to build and debug software systems or data workflows.
- A rigorous approach to evaluating statistical, quantitative, or machine learning outputs.
- Experience navigating enterprise production demands such as integrations, security, privacy, and performance.
Benefits
- Relocation assistance to new employees
Skills and tech stack
Questions candidates ask
What is the remote work and office policy for this role?
This role is based in London and uses a hybrid work model requiring three days in the office per week.
Does the company provide relocation support?
Yes, relocation assistance is offered to new employees joining this office location.
What kind of customers will this engineer work with?
The position involves partnering directly with researchers, engineers, and technical leaders at quantitative investment and trading firms.