2 days ago
London, United KingdomSenior
Responsibilities
- Partner with quantitative investment and trading firms to identify opportunities and translate needs into practical AI implementations and measurable outcomes.
- Design, build, deploy, and debug AI systems, prototypes, evaluation harnesses, reference implementations, integrations, and production accelerators.
- Make technical decisions involving models, agents, retrieval, tools, data, reliability, observability, latency, cost, safety, security, and governance.
- Diagnose implementation challenges, reproduce failures, test hypotheses, and resolve blockers.
- Help customers move from prototypes to reliable production systems, sustained adoption, and scaled impact.
- Lead technical workshops and hands-on sessions teaching quantitative researchers and engineers to apply OpenAI models and Codex.
- Translate customer deployment experience, evaluations, and feedback into requirements for Product, Research, and Engineering.
- Create reusable architectures, tooling, playbooks, and technical guidance for enterprise deployments.
Requirements
- Experience in quantitative research, quantitative development, or a closely related role, or excellent understanding of quantitative investment and trading teams.
- Practical hands-on experience with LLMs, Codex, or building AI applications.
- Substantial personal contributions to 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, machine-learning, and AI systems.
- Experience with enterprise production requirements including 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 engineers, technical leaders, security teams, product leaders, and executives.
- High agency, technical judgment, end-to-end ownership, rapid learning, and collaborative execution.
Benefits
- London-based hybrid work model requiring three days in the office per week.
- Relocation assistance is offered to new employees.
Tech Stack
Categories
Forward Deployed
About OpenAI
OpenAI builds and deploys large-scale AI models and tools—including ChatGPT, GPT-4–class models, DALL·E, and Whisper—sold via APIs and enterprise subscriptions to developers and businesses. It monetizes through usage-based API pricing and ChatGPT Plus/Team/Enterprise, and also reaches customers via Microsoft’s Azure OpenAI Service. Founded in 2015 and headquartered in San Francisco, it operates as a private partnership.