Senior Staff / Principal AI Engineer

Confirmed live in the last 24 hours

OKX

Singapore, Singapore

On-site

Posted March 16, 2026

Job Description

OKX will be prioritising applicants who have a current right to work in Singapore, and do not require OKX's sponsorship of a visa.

Who We Are

At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more.

About the Opportunity

We are seeking a Senior Staff / Principal AI Engineer to take a key technical leadership role in the design and development of OKX’s in-house Fake ID and document security system.

This is a high-impact, highly technical, and business-critical role. You will lead the 0→1 development of advanced AI models and systems that go beyond traditional vendor capabilities, with a strong focus on physical fake ID detection, document tampering detection, generative attack defense, and cross-user risk intelligence.

You will collaborate closely with Risk, Compliance, Product, and Infrastructure teams to deploy these capabilities into real-money, high-risk production environments, directly supporting OKX’s core business and security objectives.

What You’ll Be Doing

Lead the design, architecture, and delivery of OKX’s in-house Fake ID and document security platform, with a focus on adversarial and batch-based fraud scenarios
Develop and deploy computer vision models for physical fake ID detection, document tampering identification, and AIGC-generated forgery detection
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