Applied AI Engineer, Quants
Remote • London, UK • FullTime
Posted 14h ago
About the job
OpenAI's Applied AI Engineering team partners with organizations to transform frontier AI capabilities into safe, reliable, and impactful production systems. As an Applied AI Engineer focused on quantitative investment and trading firms, you will collaborate directly with researchers, engineers, and technical leaders to apply OpenAI's models and Codex to their workflows. You will combine an understanding of quantitative finance with hands-on technical skills to help customers identify opportunities, evaluate approaches, and move successful experiments into production. This role involves writing and debugging code, building evaluation systems, resolving complex integrations, and guiding decisions on model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact.
Responsibilities
- Work with quantitative investment and trading firms to identify opportunities and translate needs into practical implementations, evaluations, and measurable outcomes.
- Design, build, and deploy AI systems to solve customer problems and achieve business outcomes.
- Build prototypes, evaluation harnesses, reference implementations, integrations, and production accelerators.
- Make technical decisions regarding models, agents, retrieval, tools, data, reliability, observability, latency, cost, safety, security, and governance.
- Diagnose implementation challenges, reproduce failures, test hypotheses, and drive resolutions.
- Guide customers from prototypes to reliable production systems, sustained adoption, and scaled impact.
- Lead technical workshops to help quantitative researchers and engineers effectively apply OpenAI's models and Codex.
- Translate quant customer needs into requirements for Product, Research, and Engineering.
- Create reusable architectures, tooling, playbooks, and technical guidance to accelerate enterprise deployments.
Requirements
- Experience in quantitative research, quantitative development, or a related role, or a strong understanding of quantitative investment and trading team operations, research processes, technical environments, and evaluation expectations.
- Practical, hands-on experience with LLMs through tools like Codex or building AI applications; deep experience deploying LLM systems is valuable, but strong quant expertise and quick learning ability are prioritized.
- Substantial personal contributions in code, architecture, evaluation, debugging, or production engineering.
- High proficiency in Python and experience building and debugging research tools, data workflows, or software systems.
- Rigorous approach to evaluating quantitative, statistical, or machine-learning systems and applying this discipline to AI outputs and workflows.
- Experience navigating enterprise production requirements such as integrations, reliability, observability, security, privacy, data governance, performance, and cost.
- Ability to connect technical decisions to customer workflows, adoption, and measurable business outcomes.
- Clear and credible communication 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.
- Ability to learn quickly, challenge assumptions constructively, and collaborate with humility, with a motivation to deepen expertise in applied AI and help others adopt it.
Benefits
- Relocation assistance to new employees