OKX - Expert / Director of Al
Requirements
• Master's or PhD in Computer Science, Artificial Intelligence, or a related field (PhD preferred) • 10+ years of experience in AI/ML research, productization, or platform development, including core technical or leadership roles at globally recognized technology companies • Deep expertise in LLMs and Generative AI, deep learning, reinforcement learning, recommendation systems, and related areas • Proven track record of translating AI capabilities into measurable business outcomes • End-to-end delivery experience with autonomous AI Agent products, from inception and architecture through to production operations • Hands-on experience integrating AI deeply into engineering and development workflows — such as AI coding assistants, automated code review, or developer productivity tooling • Proficiency in Mandarin and English is required to collaborate across our multilingual, cross-regional teams • Background at an AI-native startup or frontier AI lab, with familiarity with fast-cycle product iteration • AI publications, patents, or industry-recognized technical contributions • Familiarity with the unique technical challenges of applying AI in crypto, fintech, or high-frequency data environments • All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. • If in doubt, please apply directly through our official careers website. • Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX's Candidate Privacy Notice.
Responsibilities
• Team Leadership — Build, manage, and develop a team of AI engineers and researchers; own hiring, growth, and performance across the function • Team Leadership • Agent Product Delivery — Lead end-to-end delivery of autonomous AI Agent products, covering the full lifecycle from requirements and architecture through to production operations • Agent Product Delivery • Eval-Driven Development — Establish benchmark-driven evaluation frameworks that replace manual iteration with quantifiable quality signals across agent systems • Eval-Driven Development • Agent Harness Design — Design and implement end-to-end Agent Harness infrastructure to ensure agent stability and reliability in complex, real-world scenarios • Agent Harness Design • LLM Orchestration — Orchestrate creative reasoning steps alongside deterministic guardrails, finding the right balance between flexibility and control • Orchestration • Engineering Workflow Integration — Embed AI Agent capabilities deeply into core products and engineering pipelines, improving both developer productivity and business value • Engineering Workflow Integration • Cross-functional Alignment — Collaborate with product, engineering, and business teams to ensure technical direction stays tightly aligned with strategic priorities • Cross-functional Alignment
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