About this role

▸about Binance — company background

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, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.

Role Overview

You will be responsible for production-grade AI data processing and knowledge services for Binance’s equities business. You will transform financial information—announcements, news, earnings reports, earnings call transcripts and audio/video, research reports, and more—into data, knowledge, and evidence that trading products and Binance AI can directly consume. Knowledge engineering, knowledge bases, and retrieval-augmented generation (RAG) are the core scenarios. You will also build reusable processing frameworks and own model and rule integration, task orchestration, servitization, quality control, cost management, and production stability—not just document parsing, vectorization, or model API calls.

Responsibilities:

• Own production-grade AI data processing pipelines for financial content (announcements, news, financial reports, earnings call materials, research reports, etc.), covering text, table, layout, and audio/video processing, as well as chunking, deduplication, clustering, standardization, versioning, and result validation.

• Build layered financial knowledge bases that manage source documents, structured facts, entities and events, full-text and vector indices, and relationship data; maintain metadata including source, time, security, market, language, version, and authorization.

• Engineer and operate RAG services in production—building query processing, permission and time filtering, multi-route retrieval, reranking integration, context assembly, evidence citation, and result return pipelines that ensure traceability to original sources.

• Design extensible AI data processing, knowledge engineering, and indexing frameworks with unified interfaces to adapt to new sources, formats, languages, models, rules, and algorithms; support incremental updates, index rebuilds, historical backfill, deletion, and authorization expiry.

• Integrate LLMs, document understanding models, NLP models, rule systems, and algorithm components into a unified pipeline with clear input/output contracts, task orchestration, version governance, and failure handling.

• Own engineering capabilities for AI data processing and knowledge services: APIs, async tasks, queues, caching, retry and graceful degradation, human review, canary releases, rollbacks, fault recovery, and capacity governance.

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