Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, and more. Our mission is to increase the freedom of money and financial access for people around the world.

About the Team

The Identity & Security team owns the front door to Binance. We build and operate the registration, login, and account security experience used by every one of our 300M+ users — social login, OAuth and third-party authorization, multi-factor authentication, passkeys and FIDO2, device management, account activity, and account closure.

We are currently building an in-house AI facial recognition security product for KYC verification and user safety, replacing our existing third-party vendor solution. This is a to-C product serving hundreds of millions of users, directly impacting account security and identity verification experience at scale.

Our work sits where three forces meet: enormous scale, strict security and regulatory constraints, and the reality that one extra verification step can block an attacker or lock out a legitimate user. We ship across web, mobile mini programs, and in-app webviews, and we publish the shared authentication libraries and SDKs that the rest of Binance builds on.

What You'll Do

Build the in-house AI facial recognition security product end to end — KYC verification, liveness detection, face matching — replacing the existing third-party vendor solution
Build authentication capabilities end to end: passkeys / FIDO2 (WebAuthn), authenticator apps, security keys, SMS and email verification, and biometric flows
Own the multi-factor authentication orchestration layer — verification strategy, step-up logic, and user-level customization — balancing security strength against completion rate with measurable outcomes
Collaborate closely with the AI algorithm team to integrate facial recognition, liveness detection, and other AI model capabilities into the product frontend and backend, building high-concurrency, low-latency online inference services
Maintain and evolve the shared security component and logic libraries consumed by multiple product applications, and the authentication SDKs published to teams across the company; take responsibility for bundle size, initialization behavior, and backward compatibility
Develop and operate the Node.js server-side layer: server-side rendering, route-level authentication guards, session validation and redirects, localized routing, and first-paint performance
Build and refine the BFF layer — aggregating upstream security services and turning raw error codes into results the frontend can consume and users can act on
Use funnel and event data to find drop-off in login and verification flows, and drive optimization from evidence rather than intuition
Collaborate closely with product, design, risk, customer support, backend, iOS, and Android teams; independently own the end-to-end design and delivery of a sub-domain such as passkeys, MFA orchestration, or facial recognition verification

What We're Looking For

Bachelor's or Master's degree in Computer Science, Engineering, or equivalent industry experience
3–8 years of frontend or full stack engineering experience, including consumer-facing products taken from zero to one or evolved over multiple years
Strong web fundamentals: HTML, CSS, React, JavaScript/TypeScript, browser rendering, networking, caching, and performance optimization
Production Node.js experience: server-side rendering or isomorphic applications, BFF layering, API aggregation and caching, and the ability to independently debug server-side runtime issues
A clear methodology for modeling complex flows — authentication and verification are highly branched, stateful, interruptible, and resumable, and need to be abstracted into testable, reusable models rather than nested conditionals